diff --git a/.gitignore b/.gitignore
index 505a3b1..4930d27 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,3 +8,6 @@ wheels/
# Virtual environments
.venv
+
+# Benchmarks
+benchmarks/
diff --git a/README.md b/README.md
index c803fe6..740f874 100644
--- a/README.md
+++ b/README.md
@@ -19,15 +19,10 @@ With [uv](https://docs.astral.sh/uv/), add to your project with:
uv add cells2table
```
-ONNX models need a [ONNX Runtime](https://onnxruntime.ai/getting-started) installed to run. You can install one on your own or use one of the optionals already configured.
-
| Optional | Description |
| --------------- | ----------------------- |
| `docling` | For docling usage |
| `huggingface` | For downloading models |
-| `onnx_cuda` | For NVIDIA GPUs |
-| `onnx_openvino` | For Intel GPUs and CPUs |
-| `onnx_cpu` | Default CPU runtime |
## Usage
@@ -50,7 +45,7 @@ pipeline_options = PdfPipelineOptions(
converter = DocumentConverter(
format_options={
InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options),
- InputFormat.IMAGE: PdfFormatOption(pipeline_options=pipeline_options),
+ InputFormat.IMAGE: ImageFormatOption(pipeline_options=pipeline_options),
}
)
diff --git a/cells2table/cli.py b/cells2table/cli.py
index 7d5ce2d..f4ba6f7 100644
--- a/cells2table/cli.py
+++ b/cells2table/cli.py
@@ -5,12 +5,12 @@
import cv2
from cells2table.pipelines import DefaultPipeline
-from cells2table.utils.visualize import visualize_table
+from cells2table.utils.visualize import bgr_to_rgb, rgb_to_bgr, show_image, visualize_table
logger = logging.getLogger(__name__)
-def download(local_dir: Path | str | None = None) -> None:
+def download() -> None:
"""Download default pipeline models."""
log_format = "%(asctime)s\t%(levelname)s\t%(name)s: %(message)s"
@@ -39,11 +39,12 @@ def main() -> None:
if not args.image_path.exists():
raise FileNotFoundError(f"File does not exist: {args.image_path}")
- image = cv2.imread(str(args.image_path))
-
+ image = cv2.imread(args.image_path)
if image is None:
raise ValueError(f"Failed to load image: {args.image_path}")
+ image = bgr_to_rgb(image) # ty:ignore[invalid-argument-type]
+
logger.info("Image loaded successfully from %s", args.image_path)
logger.debug(
"Image proprieties: width=%d, height=%d, channels=%d, datatype=%s",
@@ -57,7 +58,7 @@ def main() -> None:
tables = table_pipeline([image])
for table in tables:
- visualize_table(image, table) # type: ignore
+ show_image(visualize_table(rgb_to_bgr(image), table))
if __name__ == "__main__":
diff --git a/cells2table/datamodels/table.py b/cells2table/datamodels/table.py
index 6d3c5f7..c744b86 100644
--- a/cells2table/datamodels/table.py
+++ b/cells2table/datamodels/table.py
@@ -16,6 +16,22 @@ class Cell:
col_span: int = 1
+def sort_cells_index_by_top(cells: list[Cell]) -> list[int]:
+ return sorted(range(len(cells)), key=lambda i: cells[i].bbox.t)
+
+
+def sort_cells_index_by_bottom(cells: list[Cell]) -> list[int]:
+ return sorted(range(len(cells)), key=lambda i: cells[i].bbox.b)
+
+
+def sort_cells_index_by_left(cells: list[Cell]) -> list[int]:
+ return sorted(range(len(cells)), key=lambda i: cells[i].bbox.l)
+
+
+def sort_cells_index_by_right(cells: list[Cell]) -> list[int]:
+ return sorted(range(len(cells)), key=lambda i: cells[i].bbox.r)
+
+
@dataclass
class Table:
cells: list[Cell] = field(default_factory=list)
@@ -31,87 +47,87 @@ def from_detections(cls, cells_det: Iterable[DetectionResult], tolerance: float
cell = Cell(bbox=bbox, row=0, col=0)
table.cells.append(cell)
- table.compute_rows_and_cols(tolerance)
+ table.compute_structure(tolerance)
return table
- def compute_rows_and_cols(self, tolerance: float) -> None:
- self.compute_rows(tolerance)
- self.compute_cols(tolerance)
-
- def sort_cells_by_rows(self) -> None:
- self.cells = sorted(self.cells, key=lambda cell: cell.bbox.t)
-
- def sort_cells_by_cols(self) -> None:
- self.cells = sorted(self.cells, key=lambda cell: cell.bbox.l)
-
- def compute_rows(self, tolerance: float) -> None:
- self.sort_cells_by_rows()
-
- row_y = None
- row_num = 0
- row_start_idx = 0
- row_end_idx = None
- check_span_indices: set[int] = set()
-
- for i in range(len(self.cells)):
- if row_y is None:
- row_y = self.cells[i].bbox.t
-
- elif abs(self.cells[i].bbox.t - row_y) > tolerance:
- row_end_idx = i
- for j in range(row_start_idx, row_end_idx):
- self.cells[j].row = row_num
- check_span_indices.add(j)
-
- row_y = self.cells[i].bbox.t
- row_start_idx = row_end_idx
- row_num += 1
-
- for j in list(check_span_indices):
- if self.cells[j].bbox.b > row_y + tolerance:
- self.cells[j].row_span += 1
- else:
- check_span_indices.remove(j)
-
- row_end_idx = len(self.cells)
- for j in range(row_start_idx, row_end_idx):
- self.cells[j].row = row_num
- check_span_indices.add(j)
-
- self.num_rows = row_num + 1
-
- def compute_cols(self, tolerance: float) -> None:
- self.sort_cells_by_cols()
-
- col_x = None
- col_num = 0
- col_start_idx = 0
- col_end_idx = None
- check_span_indices: set[int] = set()
-
- for i in range(len(self.cells)):
- if col_x is None:
- col_x = self.cells[i].bbox.l
-
- elif abs(self.cells[i].bbox.l - col_x) > tolerance:
- col_end_idx = i
- for j in range(col_start_idx, col_end_idx):
- self.cells[j].col = col_num
- check_span_indices.add(j)
-
- col_x = self.cells[i].bbox.l
- col_start_idx = col_end_idx
- col_num += 1
-
- for j in list(check_span_indices):
- if self.cells[j].bbox.r > col_x + tolerance:
- self.cells[j].col_span += 1
- else:
- check_span_indices.remove(j)
-
- col_end_idx = len(self.cells)
- for j in range(col_start_idx, col_end_idx):
- self.cells[j].col = col_num
- check_span_indices.add(j)
-
- self.num_cols = col_num + 1
+ def compute_structure(self, tolerance: float) -> None:
+ self.compute_cells_row(tolerance)
+ self.compute_cells_col(tolerance)
+ self.compute_cells_row_span(tolerance)
+ self.compute_cells_col_span(tolerance)
+
+ def compute_cells_row(self, tolerance: float) -> None:
+ indices = sort_cells_index_by_top(self.cells)
+
+ spos = None # Spatial position
+ lpos = 0 # Logical position
+
+ for i in indices:
+ cell_spos = self.cells[i].bbox.t
+
+ if spos is None:
+ spos = cell_spos
+
+ elif cell_spos - spos > tolerance:
+ lpos += 1
+ spos = cell_spos
+
+ self.cells[i].row = lpos
+
+ self.num_rows = lpos + 1
+
+ def compute_cells_col(self, tolerance: float) -> None:
+ indices = sort_cells_index_by_left(self.cells)
+
+ spos = None # Spatial position
+ lpos = 0 # Logical position
+
+ for i in indices:
+ cell_spos = self.cells[i].bbox.l
+
+ if spos is None:
+ spos = cell_spos
+
+ elif cell_spos - spos > tolerance:
+ lpos += 1
+ spos = cell_spos
+
+ self.cells[i].col = lpos
+
+ self.num_cols = lpos + 1
+
+ def compute_cells_row_span(self, tolerance: float) -> None:
+ indices = sort_cells_index_by_bottom(self.cells)
+
+ spos = None # Spatial position
+ lpos = 0 # Logical position
+
+ for i in indices:
+ cell_spos = self.cells[i].bbox.b
+
+ if spos is None:
+ spos = cell_spos
+
+ elif cell_spos - spos > tolerance:
+ lpos += 1
+ spos = cell_spos
+
+ self.cells[i].row_span = 1 + lpos - self.cells[i].row
+
+ def compute_cells_col_span(self, tolerance: float) -> None:
+ indices = sort_cells_index_by_right(self.cells)
+
+ spos = None # Spatial position
+ lpos = 0 # Logical position
+
+ for i in indices:
+ cell_spos = self.cells[i].bbox.r
+
+ if spos is None:
+ spos = cell_spos
+
+ elif cell_spos - spos > tolerance:
+ lpos += 1
+ spos = cell_spos
+
+ self.cells[i].col_span = 1 + lpos - self.cells[i].col
diff --git a/cells2table/docling.py b/cells2table/docling.py
index 9b17b20..df70e92 100644
--- a/cells2table/docling.py
+++ b/cells2table/docling.py
@@ -1,7 +1,7 @@
-import copy
+import os
from collections.abc import Iterable
from pathlib import Path
-from typing import ClassVar, Literal, Sequence, Type
+from typing import Annotated, ClassVar, Literal, Sequence, Type
import numpy
@@ -16,50 +16,137 @@
from docling_core.types.doc.base import BoundingBox
from docling_core.types.doc.document import TableCell
from docling_core.types.doc.labels import DocItemLabel
- from docling_core.types.doc.page import BoundingRectangle, TextCellUnit
+ from docling_core.types.doc.page import TextCell
from PIL import ImageDraw
+ from pydantic import Field
+ from rtree import index
except ImportError:
raise ImportError("docling is not installed. Unable to initialize plugin.")
+from cells2table.datamodels import Table as PluginTable
from cells2table.pipelines import DefaultPipeline
-def get_tokens(page: Page, table_cluster: Cluster, scale: float) -> list[str]:
- # Check if word-level cells are available from backend:
- sp = page._backend.get_segmented_page() if page._backend else None
- if sp is not None:
- tcells = sp.get_cells_in_bbox(
- cell_unit=TextCellUnit.WORD,
- bbox=table_cluster.bbox,
- )
- if len(tcells) == 0:
- # In case word-level cells yield empty
- tcells = table_cluster.cells
- else:
- # Otherwise - we use normal (line/phrase) cells
- tcells = table_cluster.cells
- tokens = []
- for c in tcells:
- # Only allow non empty strings (spaces) into the cells of a table
- if len(c.text.strip()) > 0:
- new_cell = copy.deepcopy(c)
- new_cell.rect = BoundingRectangle.from_bounding_box(
- new_cell.rect.to_bounding_box().scaled(scale=scale)
- )
- tokens.append(
- {
- "id": new_cell.index,
- "text": new_cell.text,
- "bbox": new_cell.rect.to_bounding_box().model_dump(),
- }
- )
-
- return tokens
+def _match_text(bbox: BoundingBox, text_cells: list[TextCell], textcell_overlap: float) -> str:
+ """Return text from text_cells whose bboxes overlap sufficiently with bbox."""
+ return _match_texts([bbox], text_cells, textcell_overlap)[0]
+
+
+def _match_texts(
+ bboxes: list[BoundingBox],
+ text_cells: list[TextCell],
+ textcell_overlap: float,
+) -> list[str]:
+ """Return matched text for each bbox while preserving text-cell order."""
+ if not bboxes:
+ return []
+ if not text_cells:
+ return [""] * len(bboxes)
+
+ p = index.Property()
+ p.dimension = 2
+ spatial_index = index.Index(properties=p)
+ cell_bboxes: list[BoundingBox] = []
+
+ for cell_idx, tc in enumerate(text_cells):
+ tc_bbox = tc.rect.to_bounding_box()
+ cell_bboxes.append(tc_bbox)
+ if tc_bbox.area() > 0:
+ spatial_index.insert(cell_idx, tc_bbox.as_tuple())
+
+ matched_texts: list[str] = []
+ for bbox in bboxes:
+ overlapping = []
+ for cell_idx in sorted(spatial_index.intersection(bbox.as_tuple())):
+ tc_bbox = cell_bboxes[cell_idx]
+ if tc_bbox.get_intersection_bbox(bbox) is not None:
+ if tc_bbox.intersection_over_self(bbox) > textcell_overlap:
+ overlapping.append(text_cells[cell_idx].text.strip())
+ matched_texts.append(" ".join(overlapping))
+
+ return matched_texts
+
+
+def build_docling_table(
+ table: PluginTable,
+ table_cluster: Cluster,
+ page: Page,
+ scale: float,
+ textcell_overlap: float = 0.3,
+) -> Table:
+ cells: list[dict] = []
+
+ for cell in table.cells:
+ docling_cell_bbox: dict = {
+ "l": cell.bbox.l / scale + table_cluster.bbox.l,
+ "t": cell.bbox.t / scale + table_cluster.bbox.t,
+ "r": cell.bbox.r / scale + table_cluster.bbox.l,
+ "b": cell.bbox.b / scale + table_cluster.bbox.t,
+ "token": "",
+ }
+
+ docling_cell: dict = {
+ "bbox": docling_cell_bbox,
+ "row_span": cell.row_span,
+ "col_span": cell.col_span,
+ "start_row_offset_idx": cell.row,
+ "end_row_offset_idx": cell.row + cell.row_span,
+ "start_col_offset_idx": cell.col,
+ "end_col_offset_idx": cell.col + cell.col_span,
+ "column_header": False,
+ "row_header": False,
+ "row_section": False,
+ }
+ cells.append(docling_cell)
+
+ cell_matches = [
+ (element, BoundingBox.model_validate(element["bbox"]))
+ for element in cells
+ if element["bbox"] is not None
+ ]
+ matched_texts = _match_texts(
+ [bbox for _, bbox in cell_matches],
+ table_cluster.cells,
+ textcell_overlap,
+ )
+ for (element, bbox), text in zip(cell_matches, matched_texts):
+ element["text"] = text
+ if not text.strip() and page._backend:
+ element["text"] = page._backend.get_text_in_rect(bbox)
+
+ table_cells: list[TableCell] = []
+ for element in cells:
+ tc = TableCell.model_validate(element)
+ table_cells.append(tc)
+
+ docling_table = Table(
+ otsl_seq=[],
+ table_cells=table_cells,
+ num_rows=table.num_rows,
+ num_cols=table.num_cols,
+ id=table_cluster.id,
+ page_no=page.page_no,
+ cluster=table_cluster,
+ label=table_cluster.label,
+ )
+
+ return docling_table
class CustomDoclingTableStructureOptions(BaseTableStructureOptions):
kind: ClassVar[Literal["cells2table"]] = "cells2table"
+ confidence_threshold: Annotated[
+ float,
+ Field(
+ ge=0.0,
+ le=1.0,
+ description="Minimum confidence score to keep a cell detection.",
+ ),
+ ] = Field(
+ default_factory=lambda: float(os.environ.get("CELLS2TABLE_CONFIDENCE_THRESHOLD", "0.5"))
+ )
+
class CustomDoclingTableStructureModel(BaseTableStructureModel):
def __init__(
@@ -82,6 +169,8 @@ def __init__(
self.pipeline = DefaultPipeline(models_path)
+ self.options = options
+
# TODO: decide how to deal with accelerator options
# device = decide_device(accelerator_options.device)
@@ -96,113 +185,71 @@ def predict_tables(
conv_res: ConversionResult,
pages: Sequence[Page],
) -> Sequence[TableStructurePrediction]:
+
pages = list(pages)
predictions: list[TableStructurePrediction] = []
- for page in pages:
- assert page._backend is not None
- if not page._backend.is_valid():
- existing_prediction = page.predictions.tablestructure or TableStructurePrediction()
- page.predictions.tablestructure = existing_prediction
- predictions.append(existing_prediction)
+ table_images: list[numpy.ndarray] = []
+ table_clusters: list[Cluster] = []
+ cluster_page: list[int] = []
+
+ for i, page in enumerate(pages):
+ table_prediction = page.predictions.tablestructure or TableStructurePrediction()
+ page.predictions.tablestructure = table_prediction
+ predictions.append(table_prediction)
+
+ if (
+ page._backend is None
+ or not page._backend.is_valid()
+ or page.size is None
+ or page.predictions.layout is None
+ ):
continue
- with TimeRecorder(conv_res, "table_structure"):
- assert page.predictions.layout is not None
- assert page.size is not None
-
- table_prediction = TableStructurePrediction()
- page.predictions.tablestructure = table_prediction
-
- in_tables = [
- (
- cluster,
- [
- round(cluster.bbox.l) * self.scale,
- round(cluster.bbox.t) * self.scale,
- round(cluster.bbox.r) * self.scale,
- round(cluster.bbox.b) * self.scale,
- ],
- )
- for cluster in page.predictions.layout.clusters
- if cluster.label in [DocItemLabel.TABLE, DocItemLabel.DOCUMENT_INDEX]
+ clusters = [
+ cluster
+ for cluster in page.predictions.layout.clusters
+ if cluster.label in [DocItemLabel.TABLE, DocItemLabel.DOCUMENT_INDEX]
+ ]
+
+ if not clusters:
+ continue
+
+ cluster_page.extend([i] * len(clusters))
+ table_clusters.extend(clusters)
+
+ page_image = numpy.asarray(page.get_image(scale=self.scale))
+
+ for cluster in clusters:
+ bbox = cluster.bbox
+
+ table_image = page_image[
+ round(bbox.t * self.scale) : round(bbox.b * self.scale),
+ round(bbox.l * self.scale) : round(bbox.r * self.scale),
]
- if not in_tables:
- predictions.append(table_prediction)
- continue
-
- page_input: dict = {
- "width": page.size.width * self.scale,
- "height": page.size.height * self.scale,
- "image": numpy.asarray(page.get_image(scale=self.scale)),
- }
-
- for table_cluster, tbl_box in in_tables:
- page_input["tokens"] = get_tokens(page, table_cluster, self.scale)
-
- table_image = page_input["image"][
- round(tbl_box[1]) : round(tbl_box[3]),
- round(tbl_box[0]) : round(tbl_box[2]),
- ]
-
- table = self.pipeline([table_image])[0]
-
- docling_cells = []
-
- for cell_id, cell in enumerate(table.cells):
- docling_cell_bbox: dict = {
- "l": (cell.bbox.l + tbl_box[0]) / self.scale,
- "t": (cell.bbox.t + tbl_box[1]) / self.scale,
- "r": (cell.bbox.r + tbl_box[0]) / self.scale,
- "b": (cell.bbox.b + tbl_box[1]) / self.scale,
- "token": "",
- }
-
- docling_cell: dict = {
- "cell_id": cell_id,
- "bbox": docling_cell_bbox,
- "row_span": cell.row_span,
- "col_span": cell.col_span,
- "start_row_offset_idx": cell.row,
- "end_row_offset_idx": cell.row + cell.row_span,
- "start_col_offset_idx": cell.col,
- "end_col_offset_idx": cell.col + cell.col_span,
- "indentation_level": 0,
- "text_cell_bboxes": [docling_cell_bbox],
- "column_header": False,
- "row_header": False,
- "row_section": False,
- }
-
- bbox = BoundingBox.model_validate(docling_cell["bbox"])
-
- text_piece = page._backend.get_text_in_rect(bbox) if page._backend else ""
- docling_cell["bbox"]["token"] = text_piece
-
- tc = TableCell.model_validate(docling_cell)
- docling_cells.append(tc)
-
- docling_table = Table(
- otsl_seq=[],
- table_cells=docling_cells,
- num_rows=table.num_rows,
- num_cols=table.num_cols,
- id=table_cluster.id,
- page_no=page.page_no,
- cluster=table_cluster,
- label=table_cluster.label,
- )
- page.predictions.tablestructure.table_map[table_cluster.id] = docling_table
+ table_images.append(table_image)
- if settings.debug.visualize_tables:
- self.draw_table_and_cells(
- conv_res,
- page,
- page.predictions.tablestructure.table_map.values(),
- )
+ if len(table_images) == 0:
+ return predictions
+
+ with TimeRecorder(conv_res, "table_structure"):
+ tables = self.pipeline(table_images, self.options.confidence_threshold)
+
+ for table, cluster, page_idx in zip(tables, table_clusters, cluster_page):
+ page = pages[page_idx]
+ assert page.predictions.tablestructure is not None
+
+ docling_table = build_docling_table(table, cluster, page, self.scale)
+
+ page.predictions.tablestructure.table_map[cluster.id] = docling_table
- predictions.append(table_prediction)
+ if settings.debug.visualize_tables:
+ self.draw_table_and_cells(
+ conv_res,
+ page,
+ page.predictions.tablestructure.table_map.values(),
+ )
return predictions
diff --git a/cells2table/models/PaddlePaddle/cell_detection.py b/cells2table/models/PaddlePaddle/cell_detection.py
index 938a9c5..a88e2be 100644
--- a/cells2table/models/PaddlePaddle/cell_detection.py
+++ b/cells2table/models/PaddlePaddle/cell_detection.py
@@ -1,6 +1,7 @@
import logging
from typing import Iterable, Iterator, Sequence
+import cv2
import numpy as np
from numpy.typing import NDArray
@@ -9,7 +10,6 @@
from cells2table.utils.download import DownloadOptions, DownloadPlatform
HF_REPO_ID = "jspast/paddlepaddle-table-models-onnx"
-CONFIDENCE_THRESHOLD = 0.5
logger = logging.getLogger(__name__)
@@ -21,7 +21,11 @@ class PaddlePaddleCellDetectionModel(DetectionModel, OnnxModel):
def input_shape(self):
return self.session.get_inputs()[1].shape[2:] # assuming NCHW
- def __call__(self, input: Iterable[NDArray[np.uint8]]) -> list[Iterator[DetectionResult]]:
+ def __call__(
+ self,
+ input: Iterable[NDArray[np.uint8]],
+ conf_threshold: float = 0.5,
+ ) -> list[Iterator[DetectionResult]]:
logger.debug("Started preprocessing")
original_shapes = []
@@ -45,17 +49,29 @@ def __call__(self, input: Iterable[NDArray[np.uint8]]) -> list[Iterator[Detectio
logger.debug("Done running the model")
logger.debug("Started postprocessing")
- result = self.postprocess(output, scale_factors) # type: ignore
+ result = self.postprocess(output, scale_factors, conf_threshold) # type: ignore
logger.debug("Done postprocessing")
return result
+ def preprocess(self, input: Iterable[NDArray[np.uint8]]) -> list[NDArray[np.float32]]:
+ blob = cv2.dnn.blobFromImages(
+ input,
+ scalefactor=1 / 255.0,
+ size=self.input_shape,
+ swapRB=False,
+ crop=False,
+ ) # ty:ignore[no-matching-overload]
+
+ return list(blob)
+
@classmethod
def postprocess(
cls,
pred: NDArray,
scale_factors: Sequence[tuple[int, int]],
+ conf_threshold: float,
) -> list[Iterator[DetectionResult]]:
last_cell_idx = 0
cells = pred[0]
@@ -64,7 +80,7 @@ def postprocess(
for i, count in enumerate(pred[1]):
c = cells[last_cell_idx : last_cell_idx + count]
- c = c[c[:, 1] > CONFIDENCE_THRESHOLD]
+ c = c[c[:, 1] > conf_threshold]
last_cell_idx += count
if not c.size:
@@ -72,7 +88,7 @@ def postprocess(
continue
sx, sy = scale_factors[i]
- scores = c[:, 0]
+ scores = c[:, 1]
boxes = c[:, 2:]
boxes[:, [0, 2]] *= sy
boxes[:, [1, 3]] *= sx
diff --git a/cells2table/models/PaddlePaddle/table_classification.py b/cells2table/models/PaddlePaddle/table_classification.py
index ef24d90..0ce919d 100644
--- a/cells2table/models/PaddlePaddle/table_classification.py
+++ b/cells2table/models/PaddlePaddle/table_classification.py
@@ -1,6 +1,7 @@
import logging
from typing import Iterable, Sequence
+import cv2
import numpy as np
from numpy.typing import NDArray
@@ -26,9 +27,9 @@ def get_download_options(cls) -> DownloadOptions:
def __call__(self, input: Iterable[NDArray[np.uint8]]) -> list[ClassificationResult]:
logger.debug("Started preprocessing")
- input = self.preprocess(input)
+ images = self.preprocess(input)
- input_dict = dict(zip(self.input_names, [input]))
+ input_dict = dict(zip(self.input_names, [images]))
logger.debug("Done preprocessing")
logger.debug("Started running the model")
@@ -44,6 +45,65 @@ def __call__(self, input: Iterable[NDArray[np.uint8]]) -> list[ClassificationRes
return result
+ def preprocess(self, input: Iterable[NDArray[np.uint8]]) -> list[NDArray[np.float32]]:
+ """PP-LCNet image preprocessing pipeline.
+
+ Args:
+ input: iterable of HxWxC uint8 images (C=3, assumed RGB).
+
+ Output:
+ list of CxHxW float32 tensors (BGR order), normalized with PP-LCNet mean/std.
+ """
+ resize_short = 256 # shorter edge after resize
+ crop_size = 224 # center crop size
+ mean = np.asarray([0.406, 0.456, 0.485], dtype=np.float32) # RGB mean
+ std = np.asarray([0.225, 0.224, 0.229], dtype=np.float32) # RGB std
+ rescale_factor = 1.0 / 255.0 # uint8 -> [0,1]
+
+ out: list[NDArray[np.float32]] = []
+
+ for img in input:
+ # Validate and coerce to expected dtype/layout (HWC, uint8, 3 channels)
+ if img.ndim != 3 or img.shape[2] != 3:
+ raise ValueError(f"Expected HxWx3 image, got shape={img.shape}")
+ if img.dtype != np.uint8:
+ raise ValueError(f"Expected uint8 image, got dtype={img.dtype}")
+
+ h, w = img.shape[:2]
+
+ # Resize while preserving aspect ratio using the shorter edge as reference
+ scale = resize_short / float(min(h, w))
+ new_h = int(round(h * scale))
+ new_w = int(round(w * scale))
+
+ # Perform the resize (OpenCV expects size as (width, height))
+ resized = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
+
+ # Center-crop to crop_size x crop_size (assumes resized dims are >= crop_size)
+ if new_h < crop_size or new_w < crop_size:
+ raise ValueError(
+ f"Resized image too small for center crop: resized={new_h}x{new_w}, crop={crop_size}"
+ )
+ top = (new_h - crop_size) // 2
+ left = (new_w - crop_size) // 2
+ cropped = resized[top : top + crop_size, left : left + crop_size, :]
+
+ # Convert to float32 and rescale to [0,1]
+ x = cropped.astype(np.float32) * rescale_factor
+
+ # Normalize per channel in RGB space: (x - mean) / std
+ x = (x - mean) / std
+
+ # Convert RGB -> BGR
+ x = x[..., ::-1]
+
+ # Convert HWC -> CHW
+ x = np.transpose(x, (2, 0, 1)).astype(np.float32, copy=False)
+
+ out.append(x)
+
+ return out
+
@classmethod
def postprocess(cls, pred: Sequence[Sequence[float]]) -> list[ClassificationResult]:
return [ClassificationResult(cls.classes[np.argmax(p)], max(p)) for p in pred]
diff --git a/cells2table/models/runtimes/onnx.py b/cells2table/models/runtimes/onnx.py
index 5e887f4..f988575 100644
--- a/cells2table/models/runtimes/onnx.py
+++ b/cells2table/models/runtimes/onnx.py
@@ -1,11 +1,7 @@
from abc import ABC, abstractmethod
from pathlib import Path
-from typing import Iterable
-import cv2
-import numpy as np
import onnxruntime as ort
-from numpy.typing import NDArray
from cells2table.models.tasks.base import BaseModel
@@ -13,10 +9,6 @@
class OnnxModel(BaseModel, ABC):
"""Base interface for ONNX models."""
- scale = 1 / 255.0
- mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
- std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
-
@classmethod
@abstractmethod
def get_onnx_path(self) -> str:
@@ -49,14 +41,3 @@ def input_names(self):
@property
def output_names(self):
return [v.name for v in self.session.get_outputs()]
-
- def preprocess(self, input: Iterable[NDArray[np.uint8]]) -> list[NDArray[np.uint8]]:
- output = []
-
- for img in input:
- img = cv2.resize(img, dsize=self.input_shape, interpolation=cv2.INTER_LANCZOS4)
- img = (img.astype(np.float32) * self.scale - self.mean) / self.std # Normalize
- img = img.transpose(2, 0, 1) # HWC to CHW
- output.append(img)
-
- return output
diff --git a/cells2table/models/tasks/detection.py b/cells2table/models/tasks/detection.py
index 3ce9830..242289a 100644
--- a/cells2table/models/tasks/detection.py
+++ b/cells2table/models/tasks/detection.py
@@ -1,5 +1,5 @@
from abc import ABC, abstractmethod
-from typing import Any, Iterator, NamedTuple
+from typing import Any, Iterable, Iterator, NamedTuple
import numpy as np
@@ -13,11 +13,22 @@ class DetectionResult(NamedTuple):
confidence: float
+def filter_detections(
+ detections: Iterable[DetectionResult],
+ conf_threshold: float,
+) -> list[DetectionResult]:
+ return [d for d in detections if d.confidence > conf_threshold]
+
+
class DetectionModel(BaseModel, ABC):
"""Base interface for detection models."""
classes: list[str] = []
@abstractmethod
- def __call__(self, input: Any) -> list[Iterator[DetectionResult]]:
+ def __call__(
+ self,
+ input: Any,
+ conf_threshold: float = 0.5,
+ ) -> list[Iterator[DetectionResult]]:
pass
diff --git a/cells2table/pipelines/base.py b/cells2table/pipelines/base.py
index e1f3e56..da05ed8 100644
--- a/cells2table/pipelines/base.py
+++ b/cells2table/pipelines/base.py
@@ -11,7 +11,7 @@ def __init__(self, models_path: Path | str | None = None) -> None:
pass
@abstractmethod
- def __call__(self, input: Any):
+ def __call__(self, input: Any, **kwargs):
pass
@classmethod
diff --git a/cells2table/pipelines/classification_detection.py b/cells2table/pipelines/classification_detection.py
index f5047f4..3c79a5b 100644
--- a/cells2table/pipelines/classification_detection.py
+++ b/cells2table/pipelines/classification_detection.py
@@ -5,6 +5,7 @@
from cells2table.datamodels import Table
from cells2table.models.tasks import ClassificationModel, DetectionModel
+from cells2table.models.tasks.detection import DetectionResult, filter_detections
from cells2table.pipelines.base import BasePipeline
logger = logging.getLogger(__name__)
@@ -22,7 +23,7 @@ def __init__(self, models_path: Path | str | None = None) -> None:
pass
- def __call__(self, input: Iterable[Any]) -> list[Table]:
+ def __call__(self, input: Iterable[Any], conf_threshold: float = 0.5, **kwargs) -> list[Table]:
"""Run the pipeline."""
cls_images = [[] for c in self.classification_model.classes]
@@ -40,7 +41,7 @@ def __call__(self, input: Iterable[Any]) -> list[Table]:
# Run the detection model for each image
for i in range(len(self.classification_model.classes)):
if len(cls_images[i]):
- cls_detections[i] = self.detection_models[i](cls_images[i])
+ cls_detections[i] = self.detection_models[i](cls_images[i], conf_threshold)
# Combine results
for i in range(len(cls_result)):
@@ -51,6 +52,26 @@ def __call__(self, input: Iterable[Any]) -> list[Table]:
return output
+ def debug(
+ self,
+ image,
+ detection_model_idx: int | None = None,
+ ) -> tuple[Table, list[DetectionResult]]:
+ if detection_model_idx is None:
+ c = self.classification_model([image])[0]
+ logger.info("Image classified as %s with %.4f confidence", c.cls, c.confidence)
+
+ model_idx = self.assigned_model_idx(c.cls, self.detection_models)
+ else:
+ model_idx = detection_model_idx
+
+ d = self.detection_models[model_idx]([image], 0.0)[0]
+ d_list = list(d)
+
+ t = Table.from_detections(filter_detections(d, 0.5))
+
+ return t, d_list
+
@staticmethod
def assigned_model_idx(pred_cls: str, models: list[DetectionModel]) -> int:
"""Return the index of the first model appropriate for the class."""
diff --git a/cells2table/utils/eval/__init__.py b/cells2table/utils/eval/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/cells2table/utils/eval/cells2table_provider.py b/cells2table/utils/eval/cells2table_provider.py
new file mode 100644
index 0000000..988b28b
--- /dev/null
+++ b/cells2table/utils/eval/cells2table_provider.py
@@ -0,0 +1,260 @@
+import copy
+import logging
+from importlib.metadata import version
+from pathlib import Path
+from typing import override
+
+import numpy
+from docling.datamodel.accelerator_options import AcceleratorDevice, AcceleratorOptions
+from docling.datamodel.base_models import ConversionStatus
+from docling.utils.profiling import ProfilingItem, TimeRecorder
+from docling_core.types.doc.document import DoclingDocument, TableCell, TableData, TableItem
+from docling_core.types.doc.labels import DocItemLabel
+from docling_core.types.io import DocumentStream
+from docling_eval.datamodels.dataset_record import DatasetRecord, DatasetRecordWithPrediction
+from docling_eval.prediction_providers.base_prediction_provider import BasePredictionProvider
+from docling_eval.prediction_providers.tableformer_provider import (
+ TableFormerPredictionProvider,
+ TableFormerUpdater,
+)
+from docling_eval.utils.utils import insert_images_from_pil
+from PIL import Image
+
+from cells2table.docling import (
+ CustomDoclingTableStructureModel,
+ CustomDoclingTableStructureOptions,
+)
+
+_log = logging.getLogger(__name__)
+
+
+class Cells2tablePredictionProvider(TableFormerPredictionProvider):
+ @override
+ def __init__(
+ self,
+ num_threads: int = 12,
+ artifacts_path: Path | None = None,
+ do_visualization: bool = False,
+ ignore_missing_predictions: bool = True,
+ true_labels: set[DocItemLabel] | None = None,
+ pred_labels: set[DocItemLabel] | None = None,
+ ):
+ """
+ Initialize the cells2table prediction provider.
+
+ Args:
+ num_threads: Number of threads for prediction
+ artifacts_path: Path to artifacts
+ do_visualization: Whether to generate visualizations
+ ignore_missing_predictions: Whether to ignore missing predictions
+ true_labels: Set of DocItemLabel to use for ground truth visualization
+ pred_labels: Set of DocItemLabel to use for prediction visualization
+ """
+ BasePredictionProvider.__init__(
+ self,
+ do_visualization=do_visualization,
+ ignore_missing_predictions=ignore_missing_predictions,
+ true_labels=true_labels,
+ pred_labels=pred_labels,
+ )
+ self.tf_updater: Cells2tableUpdater = Cells2tableUpdater(num_threads, artifacts_path)
+
+ @override
+ def predict(self, record: DatasetRecord) -> DatasetRecordWithPrediction:
+ """
+ Generate a prediction for table structure.
+
+ Args:
+ record: Input dataset record
+
+ Returns:
+ Dataset record with prediction
+
+ Raises:
+ RuntimeError: If ground truth doc is not available or if mime type is unsupported
+ """
+ if record.ground_truth_doc is None:
+ raise RuntimeError(
+ "true_doc must be given for cells2table prediction provider to work."
+ )
+
+ updated = False
+ pred_doc = None
+ timings = {}
+
+ try:
+ if record.mime_type == "application/pdf":
+ if not isinstance(record.original, DocumentStream):
+ raise RuntimeError("Original document must be a DocumentStream for PDF files")
+
+ # Process PDF
+ updated, pred_doc, timings = self.tf_updater.replace_tabledata(
+ copy.deepcopy(record.original.stream), record.ground_truth_doc
+ )
+
+ elif record.mime_type == "image/png":
+ # Process image
+ updated, pred_doc, timings = self.tf_updater.replace_tabledata_with_image(
+ record.ground_truth_doc,
+ record.ground_truth_page_images,
+ )
+ else:
+ raise RuntimeError(
+ f"Unsupported mime type: {record.mime_type}. cells2table supports 'application/pdf' and 'application/png'"
+ )
+
+ pred_doc = insert_images_from_pil(
+ pred_doc,
+ record.ground_truth_pictures,
+ record.ground_truth_page_images,
+ )
+ # Set status based on update success
+ status = ConversionStatus.SUCCESS if updated else ConversionStatus.FAILURE
+
+ except Exception as e:
+ _log.error(f"Error in cells2table prediction: {str(e)}")
+ status = ConversionStatus.FAILURE
+ if not self.ignore_missing_predictions:
+ raise
+ pred_doc = record.ground_truth_doc.model_copy(
+ deep=True
+ ) # Use copy of ground truth as fallback
+
+ pred_record = self.create_dataset_record_with_prediction(record, pred_doc, timings=timings)
+ pred_record.status = status
+ return pred_record
+
+ def info(self) -> dict:
+ """Get information about the prediction provider."""
+ return {
+ "asset": "cells2table",
+ "version": version("cells2table"),
+ }
+
+
+class Cells2tableUpdater(TableFormerUpdater):
+ """
+ Utility class for updating table data using cells2table.
+
+ This class handles the prediction of table structures using the cell2table.
+ """
+
+ @override
+ def __init__(
+ self,
+ num_threads: int = 12,
+ artifacts_path: Path | None = None,
+ ):
+ """
+ Initialize the cells2table updater.
+
+ Args:
+ num_threads: Number of threads for prediction
+ artifacts_path: Path to artifacts
+ """
+ table_structure_options = CustomDoclingTableStructureOptions()
+ accelerator_options = AcceleratorOptions(
+ num_threads=num_threads, device=AcceleratorDevice.AUTO
+ )
+ self._docling_tf_model: CustomDoclingTableStructureModel = CustomDoclingTableStructureModel(
+ enabled=True,
+ artifacts_path=artifacts_path,
+ options=table_structure_options,
+ accelerator_options=accelerator_options,
+ )
+ self._docling_tf_model.scale = 1.0
+ _log.info("Initialized cells2table")
+
+ def replace_tabledata_with_image(
+ self,
+ true_doc: DoclingDocument,
+ true_page_images: list[Image.Image],
+ ) -> tuple[bool, DoclingDocument, dict[str, ProfilingItem]]:
+ pred_doc = copy.deepcopy(true_doc)
+ updated = False
+ timings: dict[str, ProfilingItem] = {}
+
+ class _TimingContainer:
+ def __init__(self, timings: dict[str, ProfilingItem]):
+ self.timings = timings
+
+ timing_container = _TimingContainer(timings)
+
+ # Ensure document has exactly one page
+ if len(pred_doc.pages) != 1:
+ _log.error("Document must have exactly one page")
+ return False, pred_doc, timings
+
+ page_size = pred_doc.pages[1].size
+ page_image = numpy.array(true_page_images[0])
+
+ # Process each table item
+ for item, level in pred_doc.iterate_items():
+ if isinstance(item, TableItem):
+ for prov in item.prov:
+ try:
+ bbox = prov.bbox.to_top_left_origin(page_image.shape[0])
+
+ # Ensure bounding box is within page bounds
+ bbox.l = max(bbox.l, 0.0)
+ bbox.t = max(bbox.t, 0.0)
+ bbox.r = min(bbox.r, page_size.width)
+ bbox.b = min(bbox.b, page_size.height)
+
+ table_image = page_image[
+ round(bbox.t) : round(bbox.b),
+ round(bbox.l) : round(bbox.r),
+ ]
+
+ with TimeRecorder(timing_container, "table_structure"): # ty:ignore[invalid-argument-type]
+ tables = self._docling_tf_model.pipeline(
+ [table_image], self._docling_tf_model.options.confidence_threshold
+ )
+
+ table = tables[0]
+
+ docling_cells = []
+
+ for cell_id, cell in enumerate(table.cells):
+ docling_cell_bbox: dict = {
+ "l": cell.bbox.l + bbox.l,
+ "t": cell.bbox.t + bbox.t,
+ "r": cell.bbox.r + bbox.l,
+ "b": cell.bbox.b + bbox.t,
+ "token": "",
+ }
+
+ docling_cell: dict = {
+ "cell_id": cell_id,
+ "bbox": docling_cell_bbox,
+ "row_span": cell.row_span,
+ "col_span": cell.col_span,
+ "start_row_offset_idx": cell.row,
+ "end_row_offset_idx": cell.row + cell.row_span,
+ "start_col_offset_idx": cell.col,
+ "end_col_offset_idx": cell.col + cell.col_span,
+ "indentation_level": 0,
+ "text_cell_bboxes": [docling_cell_bbox],
+ "column_header": False,
+ "row_header": False,
+ "row_section": False,
+ }
+
+ tc = TableCell.model_validate(docling_cell)
+ docling_cells.append(tc)
+
+ table_data: TableData = TableData(
+ num_rows=table.num_rows,
+ num_cols=table.num_cols,
+ table_cells=docling_cells,
+ )
+
+ # Update item data
+ item.data = table_data
+ updated = True
+
+ except Exception as e:
+ _log.error(f"Error predicting table: {str(e)}")
+ raise
+
+ return updated, pred_doc, timings
diff --git a/cells2table/utils/eval/external.py b/cells2table/utils/eval/external.py
new file mode 100644
index 0000000..2ab881d
--- /dev/null
+++ b/cells2table/utils/eval/external.py
@@ -0,0 +1,140 @@
+import gc
+from io import BytesIO
+from pathlib import Path
+
+import numpy as np
+from datasets import load_dataset
+from datasets.arrow_dataset import Dataset
+from docling.datamodel.accelerator_options import AcceleratorOptions
+from docling.datamodel.base_models import InputFormat
+from docling.datamodel.pipeline_options import PdfPipelineOptions, RapidOcrOptions
+from docling.document_converter import (
+ DocumentConverter,
+ FormatOption,
+ ImageFormatOption,
+ PdfFormatOption,
+)
+from docling.experimental.datamodel.table_crops_layout_options import TableCropsLayoutOptions
+from docling_core.types.io import DocumentStream
+from paddleocr import TableRecognitionPipelineV2
+from rapidocr import OCRVersion
+
+from cells2table.docling import CustomDoclingTableStructureOptions
+
+benchmarks_dir = Path(__file__).parents[3] / "benchmarks"
+
+
+def cells2table_pdfpipelineoptions(num_threads: int) -> PdfPipelineOptions:
+ options = PdfPipelineOptions()
+
+ options.allow_external_plugins = True
+ options.do_table_structure = True
+ options.layout_options = TableCropsLayoutOptions(keep_empty_clusters=True)
+ options.table_structure_options = CustomDoclingTableStructureOptions()
+ options.images_scale = 2.0
+
+ options.do_ocr = True
+ options.ocr_options = RapidOcrOptions(
+ force_full_page_ocr=True,
+ rapidocr_params={
+ "Det.ocr_version": OCRVersion.PPOCRV5,
+ "Rec.ocr_version": OCRVersion.PPOCRV5,
+ },
+ )
+
+ options.accelerator_options = AcceleratorOptions(num_threads=num_threads)
+
+ options.generate_page_images = True # Needed for visualizations
+
+ return options
+
+
+def cells2table_formatoptions(num_threads: int) -> dict[InputFormat, FormatOption]:
+ return {
+ InputFormat.PDF: PdfFormatOption(
+ pipeline_options=cells2table_pdfpipelineoptions(num_threads)
+ ),
+ InputFormat.IMAGE: ImageFormatOption(
+ pipeline_options=cells2table_pdfpipelineoptions(num_threads)
+ ),
+ }
+
+
+def create_gt(ds: Dataset, benchmark_dir: Path):
+ gt_dir = benchmark_dir / "gt"
+ gt_dir.mkdir(exist_ok=True)
+
+ for sample in ds:
+ with open(gt_dir / f"{sample['sample_id']}.html", "w") as f:
+ f.write(sample["ground_truth_html"])
+
+
+def create_pred_cells2table(ds: Dataset, benchmark_dir: Path):
+ pred_dir = benchmark_dir / "cells2table"
+ pred_dir.mkdir(exist_ok=True)
+
+ converter = DocumentConverter(format_options=cells2table_formatoptions(12))
+
+ for sample in ds:
+ with open(pred_dir / f"{sample['sample_id']}.html", "w") as f:
+ pil_image = sample["image"]
+ buf = BytesIO()
+ pil_image.save(buf, format="PNG")
+ buf.seek(0)
+
+ stream = DocumentStream(name="image.png", stream=buf)
+
+ result = converter.convert(stream)
+ f.write(result.document.tables[0].export_to_html(result.document))
+
+ if result.input and result.input._backend:
+ result.input._backend.unload()
+ del result
+ buf.close()
+ pil_image.close()
+ gc.collect()
+
+
+def create_pred_pp(ds: Dataset, benchmark_dir: Path):
+ pred_dir = benchmark_dir / "pp"
+ pred_dir.mkdir(exist_ok=True)
+
+ pipeline = TableRecognitionPipelineV2(
+ use_doc_orientation_classify=False,
+ use_doc_unwarping=False,
+ use_layout_detection=False,
+ text_detection_model_name="PP-OCRv5_mobile_det",
+ text_recognition_model_name="PP-OCRv5_mobile_rec",
+ engine="transformers",
+ )
+
+ for sample in ds:
+ with open(pred_dir / f"{sample['sample_id']}.html", "w") as f:
+ pil_image = sample["image"]
+ image = np.asarray(pil_image)
+
+ output = pipeline.predict(image)
+
+ print(output[0].html)
+ f.write(output[0].html["table_1"])
+
+ pil_image.close()
+ gc.collect()
+
+
+def main():
+ ds = load_dataset("pulse-ai/PulseBench-Tab", split="train[534:902]")
+
+ benchmark_dir = benchmarks_dir / "PulseBench-Tab"
+ benchmark_dir.mkdir(parents=True, exist_ok=True)
+
+ # create_gt(ds, benchmark_dir)
+ # create_pred_cells2table(ds, benchmark_dir)
+ create_pred_pp(ds, benchmark_dir)
+
+ # To score, run:
+ # uv run cells2table/utils/eval/tlag_scorer.py --gt benchmarks/PulseBench-Tab/gt/ --pred benchmarks/PulseBench-Tab/cells2table/ --output benchmarks/PulseBench-Tab/cells2table_scores.json
+
+
+if __name__ == "__main__":
+ main()
diff --git a/cells2table/utils/eval/main.py b/cells2table/utils/eval/main.py
new file mode 100644
index 0000000..3eab404
--- /dev/null
+++ b/cells2table/utils/eval/main.py
@@ -0,0 +1,208 @@
+import argparse
+import logging
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+import numpy as np
+from numpy.typing import NDArray
+
+from cells2table.pipelines import DefaultPipeline
+from cells2table.utils.eval.cells2table_provider import Cells2tablePredictionProvider
+from cells2table.utils.visualize import bgr_to_rgb, rgb_to_bgr, visualize_detections
+
+try:
+ from docling.datamodel.settings import settings
+ from docling_eval.cli import main as eval_main
+ from docling_eval.datamodels.types import BenchMarkNames, EvaluationModality
+ from docling_eval.dataset_builders.dataset_builder import BaseEvaluationDatasetBuilder
+ from docling_eval.dataset_builders.doclingdpbench_builder import DoclingDPBenchDatasetBuilder
+ from docling_eval.dataset_builders.omnidocbench_builder import OmniDocBenchDatasetBuilder
+ from docling_eval.dataset_builders.otsl_table_dataset_builder import (
+ FintabNetDatasetBuilder,
+ PubTabNetDatasetBuilder,
+ )
+ from docling_eval.prediction_providers.base_prediction_provider import BasePredictionProvider
+ from docling_eval.prediction_providers.tableformer_provider import TableFormerPredictionProvider
+ from docling_eval.utils.external_predictions_visualizer import PredictionsVisualizer
+ from PIL import Image
+except ImportError:
+ raise ImportError("docling-eval is not installed. Unable to initialize evaluation.")
+
+log_format = "%(asctime)s\t%(levelname)s\t%(name)s: %(message)s"
+logging.basicConfig(level=logging.INFO, format=log_format)
+
+benchmarks_dir = Path(__file__).parents[3] / "benchmarks"
+
+
+def pil_to_cv2(image: Image.Image) -> NDArray[np.uint8]:
+ return rgb_to_bgr(np.array(image))
+
+
+def cv2_to_pil(image: NDArray[np.uint8]) -> Image.Image:
+ return Image.fromarray(bgr_to_rgb(image))
+
+
+def analyze_image(
+ image: NDArray[np.uint8],
+ detection_model_idx: int | None = None,
+) -> NDArray[np.uint8]:
+ table_pipeline = DefaultPipeline()
+ table, detections = table_pipeline.debug(image, detection_model_idx)
+ return visualize_detections(image, detections) # ty:ignore[invalid-return-type]
+
+
+class BaseDataset(ABC):
+ """Base interface for datasets."""
+
+ @property
+ @abstractmethod
+ def name(self) -> BenchMarkNames:
+ pass
+
+ @property
+ @abstractmethod
+ def builder(self) -> type[BaseEvaluationDatasetBuilder]:
+ pass
+
+ @property
+ def base_dir(self) -> Path:
+ return benchmarks_dir / self.name
+
+ def create_gt(self, begin: int = 0, end: int = -1) -> None:
+ dataset = self.builder(target=self.base_dir / "gt", begin_index=begin, end_index=end) # type: ignore
+ dataset.retrieve_input_dataset()
+ dataset.save_to_disk()
+
+ def create_pred(
+ self,
+ provider: BasePredictionProvider,
+ dirname: str,
+ begin: int = 0,
+ end: int = -1,
+ ) -> None:
+ provider.create_prediction_dataset(
+ name=self.name,
+ gt_dataset_dir=self.base_dir / "gt",
+ target_dataset_dir=self.base_dir / dirname,
+ split="val" if self.name == BenchMarkNames.PUBTABNET else "test",
+ begin_index=begin,
+ end_index=end,
+ )
+
+ def evaluate(self, modalities: list[EvaluationModality], dirname: str) -> None:
+ eval_main.evaluate(
+ modality=modalities,
+ benchmark=self.name,
+ split="val" if self.name == BenchMarkNames.PUBTABNET else "test",
+ idir=self.base_dir / dirname,
+ odir=self.base_dir / dirname / "evaluations",
+ )
+
+ for modality in modalities:
+ eval_main.visualize(
+ modality=modality,
+ benchmark=self.name,
+ split="val" if self.name == BenchMarkNames.PUBTABNET else "test",
+ idir=self.base_dir / dirname,
+ odir=self.base_dir / dirname / "evaluations",
+ )
+
+ def visualize(self, dirname: str) -> None:
+ visualizer = PredictionsVisualizer(self.base_dir / dirname / "visualizations")
+ visualizer.create_visualizations(dataset_dir=self.base_dir / dirname)
+
+
+class OmniDocBench(BaseDataset):
+ @property
+ def name(self) -> BenchMarkNames:
+ return BenchMarkNames.OMNIDOCBENCH
+
+ @property
+ def builder(self) -> type[BaseEvaluationDatasetBuilder]:
+ return OmniDocBenchDatasetBuilder
+
+
+class DoclingDPBench(BaseDataset):
+ @property
+ def name(self) -> BenchMarkNames:
+ return BenchMarkNames.DOCLING_DPBENCH
+
+ @property
+ def builder(self) -> type[BaseEvaluationDatasetBuilder]:
+ return DoclingDPBenchDatasetBuilder
+
+
+class FinTabNet(BaseDataset):
+ @property
+ def name(self) -> BenchMarkNames:
+ return BenchMarkNames.FINTABNET
+
+ @property
+ def builder(self) -> type[BaseEvaluationDatasetBuilder]:
+ return FintabNetDatasetBuilder
+
+
+class PubTabNet(BaseDataset):
+ @property
+ def name(self) -> BenchMarkNames:
+ return BenchMarkNames.PUBTABNET
+
+ @property
+ def builder(self) -> type[BaseEvaluationDatasetBuilder]:
+ return PubTabNetDatasetBuilder
+
+
+def main() -> None:
+ parser = argparse.ArgumentParser()
+ parser.add_argument("provider", type=str, default="cells2table")
+ parser.add_argument("-b", "--benchmark", type=str, default="DoclingDPBench")
+ parser.add_argument("-g", "--create-gt", action="store_true", default=False)
+ parser.add_argument("-p", "--create-pred", action="store_true", default=False)
+ parser.add_argument("-e", "--evaluate", action="store_true", default=False)
+ parser.add_argument("-v", "--visualize", action="store_true", default=False)
+ parser.add_argument("-t", "--num-threads", type=int, default=4)
+ parser.add_argument("--begin", type=int, default=0)
+ parser.add_argument("--end", type=int, default=-1)
+ args = parser.parse_args()
+
+ settings.debug.profile_pipeline_timings = True
+
+ provider = None
+ match args.provider.lower():
+ case "cells2table":
+ provider = Cells2tablePredictionProvider(
+ num_threads=args.num_threads,
+ do_visualization=True,
+ )
+ case "tableformer":
+ provider = TableFormerPredictionProvider(
+ num_threads=args.num_threads,
+ do_visualization=True,
+ )
+
+ if provider is None:
+ raise ValueError(f'Unrecognized provider "{args.provider}". Unable to initialize.')
+
+ benchmark = None
+ for b in [DoclingDPBench(), OmniDocBench(), FinTabNet(), PubTabNet()]:
+ if b.name.lower() == args.benchmark.lower():
+ benchmark = b
+ break
+ if benchmark is None:
+ raise ValueError(f'Unrecognized benchmark "{args.benchmark}". Unable to initialize.')
+
+ if args.create_gt:
+ benchmark.create_gt(args.begin, args.end)
+
+ if args.create_pred:
+ benchmark.create_pred(provider, args.provider, args.begin, args.end)
+
+ if args.evaluate:
+ benchmark.evaluate([EvaluationModality.TABLE_STRUCTURE], args.provider)
+
+ if args.visualize:
+ benchmark.visualize(args.provider)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/cells2table/utils/eval/rd_scorer.py b/cells2table/utils/eval/rd_scorer.py
new file mode 100644
index 0000000..e1eda48
--- /dev/null
+++ b/cells2table/utils/eval/rd_scorer.py
@@ -0,0 +1,403 @@
+import argparse
+import json
+import os
+import re
+import sys
+
+import numpy as np
+import numpy.typing as npt
+from Levenshtein import distance as levenshtein_distance
+from lxml import etree # ty:ignore[unresolved-import]
+
+BATCH_SIZE = 150
+
+# Scoring parameters (you can adjust these as needed)
+S_ROW_MATCH = 5 # Match score for row alignment
+G_ROW = -3 # Gap penalty for row alignment (insertion/deletion of a row)
+S_CELL_MATCH = 1 # Match score for cell matching
+P_CELL_MISMATCH = -1 # Penalty for cell mismatch
+G_COL = -1 # Gap penalty for column alignment
+
+
+def cell_match_score(cell1: str | None, cell2: str | None) -> float:
+ """Compute the match score between two cells considering partial matches."""
+ if cell1 is None or cell2 is None:
+ return P_CELL_MISMATCH # Penalty for gaps or mismatches
+ if cell1 == cell2:
+ return S_CELL_MATCH # Cells are identical
+
+ # Compute the Levenshtein distance using the optimized library
+ distance = levenshtein_distance(cell1, cell2)
+ max_len = max(len(cell1), len(cell2))
+ if max_len == 0:
+ normalized_distance = 0.0 # Both cells are empty strings
+ else:
+ normalized_distance = distance / max_len
+ similarity = 1.0 - normalized_distance # Similarity between 0 and 1
+ match_score = P_CELL_MISMATCH + similarity * (S_CELL_MATCH - P_CELL_MISMATCH)
+ return match_score
+
+
+def needleman_wunsch(
+ seq1: list[str], seq2: list[str], gap_penalty: int
+) -> tuple[list[str | None], list[str | None], float]:
+ """
+ Perform Needleman-Wunsch alignment between two sequences with free end gaps.
+
+ Parameters:
+ seq1, seq2: sequences to align (lists of strings)
+ gap_penalty: penalty for gaps (insertions/deletions)
+
+ Returns:
+ alignment_a, alignment_b: aligned sequences with gaps represented by None
+ score: total alignment score
+ """
+ m = len(seq1)
+ n = len(seq2)
+
+ # Initialize the scoring matrix
+ score_matrix = np.zeros((m + 1, n + 1), dtype=np.float32)
+ traceback = np.full((m + 1, n + 1), None)
+
+ # Initialize the first row and column (no gap penalties for leading gaps)
+ for i in range(1, m + 1):
+ traceback[i, 0] = "up"
+ for j in range(1, n + 1):
+ traceback[0, j] = "left"
+
+ # Fill the rest of the matrix
+ for i in range(1, m + 1):
+ seq1_i = seq1[i - 1]
+ for j in range(1, n + 1):
+ seq2_j = seq2[j - 1]
+ match = score_matrix[i - 1, j - 1] + cell_match_score(seq1_i, seq2_j)
+ delete = score_matrix[i - 1, j] + gap_penalty
+ insert = score_matrix[i, j - 1] + gap_penalty
+ max_score = max(match, delete, insert)
+ score_matrix[i, j] = max_score
+ if max_score == match:
+ traceback[i, j] = "diag"
+ elif max_score == delete:
+ traceback[i, j] = "up"
+ else:
+ traceback[i, j] = "left"
+
+ # Traceback from the position with the highest score in the last row or column
+ i, j = m, n
+ max_score = score_matrix[i, j]
+ max_i, max_j = i, j
+ # Find the maximum score in the last row and column for free end gaps
+ last_row = score_matrix[:, n]
+ last_col = score_matrix[m, :]
+ if last_row.max() > max_score:
+ max_i = last_row.argmax()
+ max_j = n
+ max_score = last_row[max_i]
+ if last_col.max() > max_score:
+ max_i = m
+ max_j = last_col.argmax()
+ max_score = last_col[max_j]
+
+ # Traceback to get the aligned sequences
+ alignment_a: list[str | None] = []
+ alignment_b: list[str | None] = []
+ i, j = max_i, max_j
+ while i > 0 or j > 0:
+ tb_direction = traceback[i, j]
+ if i > 0 and j > 0 and tb_direction == "diag":
+ alignment_a.insert(0, seq1[i - 1])
+ alignment_b.insert(0, seq2[j - 1])
+ i -= 1
+ j -= 1
+ elif i > 0 and (j == 0 or tb_direction == "up"):
+ alignment_a.insert(0, seq1[i - 1])
+ alignment_b.insert(0, None) # Gap in seq2
+ i -= 1
+ elif j > 0 and (i == 0 or tb_direction == "left"):
+ alignment_a.insert(0, None) # Gap in seq1
+ alignment_b.insert(0, seq2[j - 1])
+ j -= 1
+ else:
+ break # Should not reach here
+
+ return alignment_a, alignment_b, max_score
+
+
+def table_similarity(ground_truth: npt.NDArray[np.str_], prediction: npt.NDArray[np.str_]) -> float:
+ """
+ Compute the similarity between two tables represented as ndarrays of strings,
+ allowing for a subset of rows at the top or bottom without penalization (to avoid penalizing subtable cropping).
+
+ Parameters:
+ ground_truth, prediction: ndarrays of strings representing the tables
+
+ Returns:
+ similarity: similarity score between 0 and 1
+ """
+
+ # Remove newlines and normalize whitespace in cells
+ def normalize_cell(cell: str) -> str:
+ return "".join(cell.replace("\n", " ").replace("-", "").split()).replace(" ", "")
+
+ # Apply normalization to both ground truth and prediction arrays
+ vectorized_normalize = np.vectorize(normalize_cell)
+ ground_truth = vectorized_normalize(ground_truth)
+ prediction = vectorized_normalize(prediction)
+
+ # Convert to lists of lists for easier manipulation
+ gt_rows = [list(row) for row in ground_truth]
+ pred_rows = [list(row) for row in prediction]
+
+ # Precompute the column alignment scores between all pairs of rows
+ m = len(gt_rows)
+ n = len(pred_rows)
+ row_match_scores = np.zeros((m, n), dtype=np.float32)
+
+ for i in range(m):
+ gt_row = gt_rows[i]
+ for j in range(n):
+ pred_row = pred_rows[j]
+ # Align columns of the two rows
+ _, _, col_score = needleman_wunsch(gt_row, pred_row, G_COL)
+ # Adjusted row match score
+ row_match_scores[i, j] = col_score + S_ROW_MATCH
+
+ # Initialize the scoring matrix for row alignment with free end gaps
+ score_matrix = np.zeros((m + 1, n + 1), dtype=np.float32)
+ traceback = np.full((m + 1, n + 1), None)
+
+ # No gap penalties for leading gaps
+ for i in range(1, m + 1):
+ traceback[i, 0] = "up"
+ for j in range(1, n + 1):
+ traceback[0, j] = "left"
+
+ # Fill the rest of the scoring matrix
+ for i in range(1, m + 1):
+ for j in range(1, n + 1):
+ match = score_matrix[i - 1, j - 1] + row_match_scores[i - 1, j - 1]
+ delete = score_matrix[i - 1, j] + G_ROW
+ insert = score_matrix[i, j - 1] + G_ROW
+ max_score = max(match, delete, insert)
+ score_matrix[i, j] = max_score
+ if max_score == match:
+ traceback[i, j] = "diag"
+ elif max_score == delete:
+ traceback[i, j] = "up"
+ else:
+ traceback[i, j] = "left"
+
+ # Traceback from the position with the highest score in the last row or column
+ i, j = m, n
+ max_score = score_matrix[i, j]
+ max_i, max_j = i, j
+ # Find the maximum score in the last row and column for free end gaps
+ last_row = score_matrix[:, n]
+ last_col = score_matrix[m, :]
+ if last_row.max() > max_score:
+ max_i = last_row.argmax()
+ max_j = n
+ max_score = last_row[max_i]
+ if last_col.max() > max_score:
+ max_i = m
+ max_j = last_col.argmax()
+ max_score = last_col[max_j]
+
+ # Traceback to get the aligned rows
+ alignment_gt_rows: list[list[str | None]] = []
+ alignment_pred_rows: list[list[str | None]] = []
+ i, j = max_i, max_j
+ while i > 0 or j > 0:
+ tb_direction = traceback[i, j]
+ if i > 0 and j > 0 and tb_direction == "diag":
+ alignment_gt_rows.insert(0, gt_rows[i - 1])
+ alignment_pred_rows.insert(0, pred_rows[j - 1])
+ i -= 1
+ j -= 1
+ elif i > 0 and (j == 0 or tb_direction == "up"):
+ alignment_gt_rows.insert(0, gt_rows[i - 1])
+ alignment_pred_rows.insert(0, [None] * len(gt_rows[i - 1])) # Gap in prediction
+ i -= 1
+ elif j > 0 and (i == 0 or tb_direction == "left"):
+ alignment_gt_rows.insert(0, [None] * len(pred_rows[j - 1])) # Gap in ground truth
+ alignment_pred_rows.insert(0, pred_rows[j - 1])
+ j -= 1
+ else:
+ break # Should not reach here
+
+ # Compute the actual total score
+ actual_total_score = max_score
+
+ # Compute the total possible score
+ num_aligned_rows = len(alignment_gt_rows)
+ if num_aligned_rows == 0:
+ return 0.0 # Avoid division by zero
+ max_row_score = num_aligned_rows * (S_ROW_MATCH + len(gt_rows[0]) * S_CELL_MATCH)
+ total_possible_score = max_row_score
+
+ # Normalize the similarity score
+ similarity = actual_total_score / total_possible_score
+ return max(0.0, min(similarity, 1.0))
+
+
+def html_to_numpy(html_string: str) -> npt.NDArray[np.str_]:
+ dom_tree = etree.HTML(html_string, parser=etree.HTMLParser())
+ table_rows: list[list[str]] = []
+ span_info: dict[int, tuple[str, int]] = {}
+
+ for table_row in dom_tree.xpath("//tr"):
+ current_row: list[str] = []
+ column_index = 0
+
+ while span_info.get(column_index, (None, 0))[1] > 0:
+ current_row.append(span_info[column_index][0])
+ span_info[column_index] = (
+ span_info[column_index][0],
+ span_info[column_index][1] - 1,
+ )
+ if span_info[column_index][1] == 0:
+ del span_info[column_index]
+ column_index += 1
+
+ for table_cell in table_row.xpath("td|th"):
+ while span_info.get(column_index, (None, 0))[1] > 0:
+ current_row.append(span_info[column_index][0])
+ span_info[column_index] = (
+ span_info[column_index][0],
+ span_info[column_index][1] - 1,
+ )
+ if span_info[column_index][1] == 0:
+ del span_info[column_index]
+ column_index += 1
+
+ row_span = int(table_cell.get("rowspan", "1"))
+ col_span = int(table_cell.get("colspan", "1"))
+ cell_text = "".join(table_cell.itertext()).strip()
+
+ if row_span > 1:
+ for i in range(col_span):
+ span_info[column_index + i] = (cell_text, row_span - 1)
+
+ for _ in range(col_span):
+ current_row.append(cell_text)
+ column_index += col_span
+
+ while span_info.get(column_index, (None, 0))[1] > 0:
+ current_row.append(span_info[column_index][0])
+ span_info[column_index] = (
+ span_info[column_index][0],
+ span_info[column_index][1] - 1,
+ )
+ if span_info[column_index][1] == 0:
+ del span_info[column_index]
+ column_index += 1
+
+ table_rows.append(current_row)
+
+ max_columns = max(map(len, table_rows)) if table_rows else 0
+ for row in table_rows:
+ row.extend([""] * (max_columns - len(row)))
+
+ return np.array(table_rows)
+
+
+def clean_table_in_html(html: str) -> str:
+ # Find the first block (including newlines)
+ table_match = re.search(r"]*>.*?", html, re.DOTALL | re.IGNORECASE)
+
+ if not table_match:
+ return ""
+
+ table_html = table_match.group(0)
+
+ # Remove and tags (case-insensitive)
+ table_html = re.sub(r"]*>", "", table_html, flags=re.IGNORECASE)
+
+ return table_html
+
+
+def main():
+ parser = argparse.ArgumentParser(description="T-LAG scorer for PulseBench-Tab")
+ parser.add_argument("--gt", required=True, help="Directory with ground truth HTML files")
+ parser.add_argument("--pred", required=True, help="Directory with predicted HTML files")
+ parser.add_argument("--output", default=None, help="Output JSON file (default: stdout)")
+ parser.add_argument("--workers", type=int, default=8, help="Number of parallel workers")
+ args = parser.parse_args()
+
+ gt_files = {f.replace(".html", "") for f in os.listdir(args.gt) if f.endswith(".html")}
+ pred_files = {f.replace(".html", "") for f in os.listdir(args.pred) if f.endswith(".html")}
+
+ common = sorted(gt_files & pred_files)
+ missing = sorted(gt_files - pred_files)
+
+ print(f"Ground truth: {len(gt_files)} files", file=sys.stderr)
+ print(f"Predictions: {len(pred_files)} files", file=sys.stderr)
+ print(f"Common: {len(common)} files", file=sys.stderr)
+ print(f"Missing: {len(missing)} files", file=sys.stderr)
+
+ tasks = [
+ (sid, os.path.join(args.gt, f"{sid}.html"), os.path.join(args.pred, f"{sid}.html"))
+ for sid in common
+ ]
+
+ scores = {}
+ for sample_id, gt_path, pred_path in tasks:
+ try:
+ with open(gt_path) as f:
+ gt_html = f.read()
+ with open(pred_path) as f:
+ pred_html = f.read()
+ if not pred_html.strip():
+ continue
+
+ gt_table = html_to_numpy(clean_table_in_html(gt_html))
+ pred_table = html_to_numpy(clean_table_in_html(pred_html))
+
+ scores[sample_id] = float(table_similarity(gt_table, pred_table))
+
+ except Exception as e:
+ print(e, "\n", clean_table_in_html(gt_html), "\n", clean_table_in_html(pred_html))
+
+ # Compute aggregate stats
+ valid_scores = [s for s in scores.values()]
+ arr = np.array(valid_scores)
+
+ summary = {
+ "n_total": len(gt_files),
+ "n_scored": len(valid_scores),
+ "n_missing": len(gt_files) - len(valid_scores),
+ "coverage_pct": round(len(valid_scores) / len(gt_files) * 100, 1),
+ "mean": round(float(np.mean(arr)), 4) if len(arr) > 0 else None,
+ "median": round(float(np.median(arr)), 4) if len(arr) > 0 else None,
+ "std": round(float(np.std(arr)), 4) if len(arr) > 0 else None,
+ "p10": round(float(np.percentile(arr, 10)), 4) if len(arr) > 0 else None,
+ "p25": round(float(np.percentile(arr, 25)), 4) if len(arr) > 0 else None,
+ "p75": round(float(np.percentile(arr, 75)), 4) if len(arr) > 0 else None,
+ "p90": round(float(np.percentile(arr, 90)), 4) if len(arr) > 0 else None,
+ "perfect_count": int(np.sum(arr >= 0.9999)) if len(arr) > 0 else 0,
+ }
+
+ output = {
+ "summary": summary,
+ "per_sample": {sid: round(s, 6) for sid, s in scores.items()},
+ }
+
+ # Print summary to stderr
+ print("\nResults:", file=sys.stderr)
+ print(f" T-LAG Score (mean): {summary['mean']}", file=sys.stderr)
+ print(f" Median: {summary['median']}", file=sys.stderr)
+ print(f" Coverage: {summary['coverage_pct']}%", file=sys.stderr)
+ print(f" Perfect (=1.0): {summary['perfect_count']}", file=sys.stderr)
+
+ # Output JSON
+ json_str = json.dumps(output, indent=2, ensure_ascii=False)
+ if args.output:
+ with open(args.output, "w") as f:
+ f.write(json_str)
+ print(f"\nSaved to {args.output}", file=sys.stderr)
+ else:
+ print(json_str)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/cells2table/utils/eval/teds_scorer.py b/cells2table/utils/eval/teds_scorer.py
new file mode 100644
index 0000000..7b1c2e4
--- /dev/null
+++ b/cells2table/utils/eval/teds_scorer.py
@@ -0,0 +1,119 @@
+import argparse
+import json
+import os
+import re
+import sys
+
+import numpy as np
+from docling_metrics_table import TableMetric, TableMetricHTMLInputSample
+from docling_metrics_table.docling_metrics_table import TableMetricKind
+
+
+def clean_table_in_html(html: str) -> str:
+ # Find the first block (including newlines)
+ table_match = re.search(r"]*>.*?", html, re.DOTALL | re.IGNORECASE)
+
+ if not table_match:
+ return ""
+
+ table_html = table_match.group(0)
+
+ # Remove and tags (case-insensitive)
+ table_html = re.sub(r"]*>", "", table_html, flags=re.IGNORECASE)
+
+ return table_html
+
+
+def main():
+ parser = argparse.ArgumentParser(description="T-LAG scorer for PulseBench-Tab")
+ parser.add_argument("--gt", required=True, help="Directory with ground truth HTML files")
+ parser.add_argument("--pred", required=True, help="Directory with predicted HTML files")
+ parser.add_argument("--output", default=None, help="Output JSON file (default: stdout)")
+ parser.add_argument("--workers", type=int, default=8, help="Number of parallel workers")
+ args = parser.parse_args()
+
+ gt_files = {f.replace(".html", "") for f in os.listdir(args.gt) if f.endswith(".html")}
+ pred_files = {f.replace(".html", "") for f in os.listdir(args.pred) if f.endswith(".html")}
+
+ common = sorted(gt_files & pred_files)
+ missing = sorted(gt_files - pred_files)
+
+ print(f"Ground truth: {len(gt_files)} files", file=sys.stderr)
+ print(f"Predictions: {len(pred_files)} files", file=sys.stderr)
+ print(f"Common: {len(common)} files", file=sys.stderr)
+ print(f"Missing: {len(missing)} files", file=sys.stderr)
+
+ tasks = [
+ (sid, os.path.join(args.gt, f"{sid}.html"), os.path.join(args.pred, f"{sid}.html"))
+ for sid in common
+ ]
+
+ table_metric = TableMetric([TableMetricKind.TEDS])
+
+ scores = {}
+ for sample_id, gt_path, pred_path in tasks:
+ try:
+ with open(gt_path) as f:
+ gt_html = f.read()
+ with open(pred_path) as f:
+ pred_html = f.read()
+ if not pred_html.strip():
+ continue
+
+ html_sample = TableMetricHTMLInputSample(
+ id=sample_id,
+ html_a=clean_table_in_html(gt_html),
+ html_b=clean_table_in_html(pred_html),
+ structure_only=True,
+ )
+ html_evaluation = table_metric.evaluate_sample(html_sample)
+ if html_evaluation.teds:
+ scores[sample_id] = html_evaluation.teds.teds
+
+ except Exception as e:
+ print(e, "\n", clean_table_in_html(gt_html), "\n", clean_table_in_html(pred_html))
+ # return
+
+ # Compute aggregate stats
+ valid_scores = [s for s in scores.values()]
+ arr = np.array(valid_scores)
+
+ summary = {
+ "n_total": len(gt_files),
+ "n_scored": len(valid_scores),
+ "n_missing": len(gt_files) - len(valid_scores),
+ "coverage_pct": round(len(valid_scores) / len(gt_files) * 100, 1),
+ "mean": round(float(np.mean(arr)), 4) if len(arr) > 0 else None,
+ "median": round(float(np.median(arr)), 4) if len(arr) > 0 else None,
+ "std": round(float(np.std(arr)), 4) if len(arr) > 0 else None,
+ "p10": round(float(np.percentile(arr, 10)), 4) if len(arr) > 0 else None,
+ "p25": round(float(np.percentile(arr, 25)), 4) if len(arr) > 0 else None,
+ "p75": round(float(np.percentile(arr, 75)), 4) if len(arr) > 0 else None,
+ "p90": round(float(np.percentile(arr, 90)), 4) if len(arr) > 0 else None,
+ "perfect_count": int(np.sum(arr >= 0.9999)) if len(arr) > 0 else 0,
+ }
+
+ output = {
+ "summary": summary,
+ "per_sample": {sid: round(s, 6) for sid, s in scores.items()},
+ }
+
+ # Print summary to stderr
+ print("\nResults:", file=sys.stderr)
+ print(f" T-LAG Score (mean): {summary['mean']}", file=sys.stderr)
+ print(f" Median: {summary['median']}", file=sys.stderr)
+ print(f" Coverage: {summary['coverage_pct']}%", file=sys.stderr)
+ print(f" Perfect (=1.0): {summary['perfect_count']}", file=sys.stderr)
+
+ # Output JSON
+ json_str = json.dumps(output, indent=2, ensure_ascii=False)
+ if args.output:
+ with open(args.output, "w") as f:
+ f.write(json_str)
+ print(f"\nSaved to {args.output}", file=sys.stderr)
+ else:
+ print(json_str)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/cells2table/utils/eval/tlag_scorer.py b/cells2table/utils/eval/tlag_scorer.py
new file mode 100644
index 0000000..e4fa587
--- /dev/null
+++ b/cells2table/utils/eval/tlag_scorer.py
@@ -0,0 +1,360 @@
+#!/usr/bin/env python3
+"""
+T-LAG (Table Logical Adjacency Graph) scorer for PulseBench-Tab.
+
+Scores predicted HTML tables against ground truth by modeling tables as
+2D directed graphs and computing F1 on optimally matched edges.
+
+Usage:
+ python tlag_scorer.py --gt ground_truth/ --pred predictions/ [--workers 8]
+
+Each directory should contain HTML files named {sample_id}.html.
+"""
+
+import argparse
+import json
+import os
+import re
+import sys
+from collections import defaultdict
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from dataclasses import dataclass
+
+import numpy as np
+from lxml import etree # ty:ignore[unresolved-import]
+from Levenshtein import distance as levenshtein_distance
+from scipy.optimize import linear_sum_assignment
+
+# -- Configuration --
+
+EXPONENT = 7
+_NULL_MARKERS = frozenset([
+ "", "-", "\u2013", "\u2014", "...", "\u2026",
+ "n/a", "na", "none", "nil", "--", "---",
+])
+
+
+# -- Data structures --
+
+@dataclass
+class Edge:
+ source: int
+ target: int
+ direction: str
+ source_text: str
+ target_text: str
+
+
+# -- Text normalization and similarity --
+
+def normalize_text(text):
+ """Normalize cell text for comparison."""
+ if text is None:
+ return "[NULL]"
+ t = text.strip()
+ if t.lower() in _NULL_MARKERS or t.replace(" ", "").replace("\u00a0", "") == "":
+ return "[NULL]"
+ t = re.sub(r'[\u2012\u2013\u2014\u2015\u2212]', '-', t)
+ t = t.replace("\u00a0", " ")
+ t = re.sub(r'\s+', ' ', t).strip()
+ return t
+
+
+def psi(text_gt, text_pred):
+ """
+ Text similarity kernel.
+
+ Psi(a, b) = (1 - d_Lev(a, b) / max(|a|, |b|))^k
+
+ where k = EXPONENT (default 7).
+ """
+ gt, pred = normalize_text(text_gt), normalize_text(text_pred)
+ if gt == "[NULL]" and pred == "[NULL]":
+ return 1.0
+ if gt == "[NULL]" or pred == "[NULL]":
+ return 0.0
+ dist = levenshtein_distance(gt, pred)
+ max_len = max(len(gt), len(pred))
+ if max_len == 0:
+ return 1.0
+ return (1.0 - dist / max_len) ** EXPONENT
+
+
+# -- HTML parsing --
+
+def parse_html_to_matrix(html_string):
+ """
+ Parse an HTML table into a cell-position grid matrix.
+
+ Returns (cells, matrix, num_rows, num_cols) where:
+ - cells: dict mapping cell_id to {id, text, rs, cs}
+ - matrix: dict mapping (row, col) to cell_id
+ - num_rows, num_cols: grid dimensions
+ """
+ dom = etree.HTML(html_string, parser=etree.HTMLParser())
+ cells, cell_counter, matrix, span_info = {}, 0, {}, {}
+ row_idx = 0
+ all_trs = dom.xpath("//tr")
+ total_rows = len(all_trs)
+
+ for tr in all_trs:
+ col_idx = 0
+ while col_idx in span_info and span_info[col_idx][1] > 0:
+ cid, remaining = span_info[col_idx]
+ matrix[(row_idx, col_idx)] = cid
+ span_info[col_idx] = (cid, remaining - 1)
+ if span_info[col_idx][1] == 0:
+ del span_info[col_idx]
+ col_idx += 1
+ for cell_elem in tr.xpath("td|th"):
+ while col_idx in span_info and span_info[col_idx][1] > 0:
+ cid, remaining = span_info[col_idx]
+ matrix[(row_idx, col_idx)] = cid
+ span_info[col_idx] = (cid, remaining - 1)
+ if span_info[col_idx][1] == 0:
+ del span_info[col_idx]
+ col_idx += 1
+ try:
+ rs = int(cell_elem.get("rowspan", "1").strip().rstrip('>'))
+ except ValueError:
+ rs = 1
+ if rs == 0:
+ rs = max(1, total_rows - row_idx)
+ try:
+ cs = int(cell_elem.get("colspan", "1").strip().rstrip('>'))
+ except ValueError:
+ cs = 1
+ if cs == 0:
+ cs = 1
+ text = "".join(cell_elem.itertext()).strip()
+ cells[cell_counter] = {"id": cell_counter, "text": text, "rs": rs, "cs": cs}
+ for r in range(rs):
+ for c in range(cs):
+ matrix[(row_idx + r, col_idx + c)] = cell_counter
+ if rs > 1:
+ for c in range(cs):
+ span_info[col_idx + c] = (cell_counter, rs - 1)
+ col_idx += cs
+ cell_counter += 1
+ while col_idx in span_info and span_info[col_idx][1] > 0:
+ cid, remaining = span_info[col_idx]
+ matrix[(row_idx, col_idx)] = cid
+ span_info[col_idx] = (cid, remaining - 1)
+ if span_info[col_idx][1] == 0:
+ del span_info[col_idx]
+ col_idx += 1
+ row_idx += 1
+
+ if not matrix:
+ return cells, matrix, 0, 0
+ num_rows = max(r for r, c in matrix.keys()) + 1
+ num_cols = max(c for r, c in matrix.keys()) + 1
+ return cells, matrix, num_rows, num_cols
+
+
+# -- Edge extraction --
+
+def extract_edges(cells, matrix, num_rows, num_cols):
+ """
+ Extract directed edges from a cell-position grid matrix.
+
+ RIGHT edge: cell at (r, c) -> cell at (r, c+1), if they are different cells.
+ BELOW edge: cell at (r, c) -> cell at (r+1, c), if they are different cells.
+
+ Edges are deduplicated by (source_id, target_id, direction) to prevent
+ spanning cells from generating duplicate edges.
+ """
+ seen, edges = set(), []
+ for r in range(num_rows):
+ for c in range(num_cols):
+ if (r, c) not in matrix:
+ continue
+ uid = matrix[(r, c)]
+ if c + 1 < num_cols and (r, c + 1) in matrix:
+ vid = matrix[(r, c + 1)]
+ if uid != vid:
+ key = (uid, vid, "RIGHT")
+ if key not in seen:
+ seen.add(key)
+ edges.append(Edge(uid, vid, "RIGHT",
+ cells[uid]["text"], cells[vid]["text"]))
+ if r + 1 < num_rows and (r + 1, c) in matrix:
+ vid = matrix[(r + 1, c)]
+ if uid != vid:
+ key = (uid, vid, "BELOW")
+ if key not in seen:
+ seen.add(key)
+ edges.append(Edge(uid, vid, "BELOW",
+ cells[uid]["text"], cells[vid]["text"]))
+ return edges
+
+
+# -- Scoring --
+
+def compute_tlag(gt_edges, pr_edges):
+ """
+ Compute T-LAG score (F1 on optimally matched edges).
+
+ Uses the Hungarian algorithm for globally optimal one-to-one matching.
+ RIGHT edges only match RIGHT; BELOW only matches BELOW.
+
+ Returns (f1, precision, recall).
+ """
+ n_gt, n_pred = len(gt_edges), len(pr_edges)
+ if n_gt == 0 and n_pred == 0:
+ return 1.0, 1.0, 1.0
+ if n_gt == 0 or n_pred == 0:
+ return 0.0, 0.0, 0.0
+
+ weight = np.zeros((n_gt, n_pred), dtype=np.float64)
+ for i, eg in enumerate(gt_edges):
+ for j, ep in enumerate(pr_edges):
+ if eg.direction != ep.direction:
+ continue
+ s = psi(eg.source_text, ep.source_text)
+ t = psi(eg.target_text, ep.target_text)
+ weight[i, j] = s * t
+
+ row_ind, col_ind = linear_sum_assignment(-weight)
+ matched = sum(weight[r, c] for r, c in zip(row_ind, col_ind))
+ precision = matched / n_pred
+ recall = matched / n_gt
+ f1 = (2 * precision * recall / (precision + recall)) if (precision + recall) > 0 else 0.0
+ return f1, precision, recall
+
+
+def score_single(gt_html, pred_html):
+ """
+ Score a single predicted HTML table against ground truth.
+
+ Returns a dict with keys: score, precision, recall, gt_edges, pred_edges.
+ """
+ gt_cells, gt_mat, gt_r, gt_c = parse_html_to_matrix(gt_html)
+ pr_cells, pr_mat, pr_r, pr_c = parse_html_to_matrix(pred_html)
+
+ gt_edges = extract_edges(gt_cells, gt_mat, gt_r, gt_c)
+ pr_edges = extract_edges(pr_cells, pr_mat, pr_r, pr_c)
+
+ # Single-cell fallback: 0 edges on both sides
+ if len(gt_edges) == 0 and len(pr_edges) == 0:
+ gt_text = gt_cells[0]["text"] if gt_cells else ""
+ pr_text = pr_cells[0]["text"] if pr_cells else ""
+ score = psi(gt_text, pr_text)
+ return {
+ "score": score,
+ "precision": score,
+ "recall": score,
+ "gt_edges": 0,
+ "pred_edges": 0,
+ }
+
+ f1, prec, rec = compute_tlag(gt_edges, pr_edges)
+ return {
+ "score": f1,
+ "precision": prec,
+ "recall": rec,
+ "gt_edges": len(gt_edges),
+ "pred_edges": len(pr_edges),
+ }
+
+
+def _score_file(args):
+ """Worker function for parallel scoring."""
+ sample_id, gt_path, pred_path = args
+ try:
+ with open(gt_path) as f:
+ gt_html = f.read()
+ with open(pred_path) as f:
+ pred_html = f.read()
+ if not pred_html.strip():
+ return sample_id, None
+ result = score_single(gt_html, pred_html)
+ return sample_id, result
+ except Exception as e:
+ return sample_id, None
+
+
+# -- CLI --
+
+def main():
+ parser = argparse.ArgumentParser(
+ description="T-LAG scorer for PulseBench-Tab"
+ )
+ parser.add_argument("--gt", required=True, help="Directory with ground truth HTML files")
+ parser.add_argument("--pred", required=True, help="Directory with predicted HTML files")
+ parser.add_argument("--output", default=None, help="Output JSON file (default: stdout)")
+ parser.add_argument("--workers", type=int, default=8, help="Number of parallel workers")
+ args = parser.parse_args()
+
+ gt_files = {f.replace(".html", "") for f in os.listdir(args.gt) if f.endswith(".html")}
+ pred_files = {f.replace(".html", "") for f in os.listdir(args.pred) if f.endswith(".html")}
+
+ common = sorted(gt_files & pred_files)
+ missing = sorted(gt_files - pred_files)
+
+ print(f"Ground truth: {len(gt_files)} files", file=sys.stderr)
+ print(f"Predictions: {len(pred_files)} files", file=sys.stderr)
+ print(f"Common: {len(common)} files", file=sys.stderr)
+ print(f"Missing: {len(missing)} files", file=sys.stderr)
+
+ tasks = [
+ (sid, os.path.join(args.gt, f"{sid}.html"), os.path.join(args.pred, f"{sid}.html"))
+ for sid in common
+ ]
+
+ scores = {}
+ with ProcessPoolExecutor(max_workers=args.workers) as executor:
+ futures = {executor.submit(_score_file, t): t[0] for t in tasks}
+ done = 0
+ for future in as_completed(futures):
+ sid = futures[future]
+ sample_id, result = future.result()
+ if result is not None:
+ scores[sample_id] = result
+ done += 1
+ if done % 500 == 0:
+ print(f" Scored {done}/{len(tasks)}...", file=sys.stderr)
+
+ # Compute aggregate stats
+ valid_scores = [s["score"] for s in scores.values()]
+ arr = np.array(valid_scores)
+
+ summary = {
+ "n_total": len(gt_files),
+ "n_scored": len(valid_scores),
+ "n_missing": len(gt_files) - len(valid_scores),
+ "coverage_pct": round(len(valid_scores) / len(gt_files) * 100, 1),
+ "mean": round(float(np.mean(arr)), 4) if len(arr) > 0 else None,
+ "median": round(float(np.median(arr)), 4) if len(arr) > 0 else None,
+ "std": round(float(np.std(arr)), 4) if len(arr) > 0 else None,
+ "p10": round(float(np.percentile(arr, 10)), 4) if len(arr) > 0 else None,
+ "p25": round(float(np.percentile(arr, 25)), 4) if len(arr) > 0 else None,
+ "p75": round(float(np.percentile(arr, 75)), 4) if len(arr) > 0 else None,
+ "p90": round(float(np.percentile(arr, 90)), 4) if len(arr) > 0 else None,
+ "perfect_count": int(np.sum(arr >= 0.9999)) if len(arr) > 0 else 0,
+ }
+
+ output = {
+ "summary": summary,
+ "per_sample": {sid: round(s["score"], 6) for sid, s in scores.items()},
+ }
+
+ # Print summary to stderr
+ print(f"\nResults:", file=sys.stderr)
+ print(f" T-LAG Score (mean): {summary['mean']}", file=sys.stderr)
+ print(f" Median: {summary['median']}", file=sys.stderr)
+ print(f" Coverage: {summary['coverage_pct']}%", file=sys.stderr)
+ print(f" Perfect (=1.0): {summary['perfect_count']}", file=sys.stderr)
+
+ # Output JSON
+ json_str = json.dumps(output, indent=2, ensure_ascii=False)
+ if args.output:
+ with open(args.output, "w") as f:
+ f.write(json_str)
+ print(f"\nSaved to {args.output}", file=sys.stderr)
+ else:
+ print(json_str)
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/cells2table/utils/visualize.py b/cells2table/utils/visualize.py
index e58d802..f6f005d 100644
--- a/cells2table/utils/visualize.py
+++ b/cells2table/utils/visualize.py
@@ -3,6 +3,27 @@
from numpy.typing import NDArray
from cells2table.datamodels import Table
+from cells2table.models.tasks.detection import DetectionResult
+
+
+def bgr_to_rgb(image: NDArray[np.uint8]) -> NDArray[np.uint8]:
+ return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # ty:ignore[invalid-return-type]
+
+
+def rgb_to_bgr(image: NDArray[np.uint8]) -> NDArray[np.uint8]:
+ return cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # ty:ignore[invalid-return-type]
+
+
+def show_image(image: NDArray[np.uint8], window_title: str = "Image") -> None:
+ """Create a window to show an image.
+
+ Args:
+ image: A cv2 BGR image.
+ window_title: The title of the created window.
+ """
+ cv2.imshow(window_title, image)
+ cv2.waitKey(0)
+ cv2.destroyAllWindows()
def visualize_table(
@@ -10,13 +31,18 @@ def visualize_table(
table: Table,
color=(0, 255, 0),
thickness=2,
- window_name="Bounding Boxes",
-) -> None:
- """Simple table visualization on top of the image.
+) -> NDArray[np.uint8]:
+ """Simple table cells visualization on top of the image.
- image: np.ndarray (BGR image loaded with cv2)
- """
+ The Row, Col, Row Span and Col Span will be printed for each cell.
+ The format is `R,C : RS,CS`.
+ Args:
+ image: A cv2 BGR image of the table.
+ table: The table to draw on top of the image.
+ color: The color of the table overlay.
+ thickness: The thickness of the overlay lines.
+ """
img = image.copy()
for cell in table.cells:
@@ -37,6 +63,77 @@ def visualize_table(
thickness,
)
- cv2.imshow(window_name, img)
- cv2.waitKey(0)
- cv2.destroyAllWindows()
+ return img
+
+
+def visualize_detections(
+ image: NDArray[np.uint8],
+ detections: list[DetectionResult],
+) -> None:
+ """Detections visualization on top of the image.
+
+ Args:
+ image: A cv2 image of the table.
+ table: The table to draw on top of the image.
+ color: The color of the table overlay.
+ thickness: The thickness of the overlay lines.
+ """
+
+ import matplotlib.cm as cm
+ import matplotlib.colors as mcolors
+ import matplotlib.patches as patches
+ import matplotlib.pyplot as plt
+ from matplotlib.widgets import RangeSlider
+
+ img = bgr_to_rgb(image.copy())
+
+ # Normalize + colormap
+ norm = mcolors.Normalize(vmin=0, vmax=1)
+ cmap = cm.get_cmap("winter")
+
+ # Create figure
+ fig, ax = plt.subplots()
+ plt.subplots_adjust(bottom=0.25)
+ ax.imshow(img)
+ ax.axis("off")
+
+ # Store rectangle artists
+ rects = []
+ texts = []
+
+ for bbox, conf in detections:
+ color = cmap(norm(conf))
+ rect = patches.Rectangle(
+ (bbox[0], bbox[1]),
+ bbox[2] - bbox[0],
+ bbox[3] - bbox[1],
+ linewidth=2,
+ edgecolor=color,
+ facecolor="none",
+ )
+ ax.add_patch(rect)
+ rects.append((rect, conf))
+
+ txt = ax.text(bbox[0], bbox[1] - 5, f"{conf:.2f}", color=color)
+ texts.append((txt, conf))
+
+ # Add range slider
+ slider_ax = plt.axes((0.2, 0.1, 0.6, 0.03))
+ slider = RangeSlider(slider_ax, "Confidence", 0.0, 1.0, valinit=(0.5, 1.0))
+
+ # Update function
+ def update(val):
+ min_conf, max_conf = slider.val
+
+ for rect, conf in rects:
+ rect.set_visible(min_conf <= conf <= max_conf)
+
+ for txt, conf in texts:
+ txt.set_visible(min_conf <= conf <= max_conf)
+
+ fig.canvas.draw_idle()
+
+ update(slider.val)
+ slider.on_changed(update)
+
+ plt.show()
diff --git a/eval/DoclingDPBench_TableFormer.json b/eval/DoclingDPBench_TableFormer.json
new file mode 100644
index 0000000..3529c16
--- /dev/null
+++ b/eval/DoclingDPBench_TableFormer.json
@@ -0,0 +1,606 @@
+{
+ "benchmark_name": "DoclingDPBench",
+ "provider_name": "TableFormer",
+ "TEDS_mean": 0.9220185185185185,
+ "TEDS_median": 1.0,
+ "TEDS_std": 0.21949871262093917,
+ "timings_mean": 1.1154176691295983,
+ "timings_std": 0.6374344263132518,
+ "num_evaluations": 54,
+ "evaluations": [
+ {
+ "TEDS": 1.0,
+ "filename": "doc_603d71e12f52d9801a9d82995babf69681d923f493b8d49abfbab8662a88b376_page_000001.png",
+ "is_complex": false,
+ "pred_ncols": 5,
+ "pred_nrows": 5,
+ "table_id": 1,
+ "true_ncols": 5,
+ "true_nrows": 5,
+ "timing": 0.942987149000146
+ },
+ {
+ "TEDS": 1.0,
+ "filename": "doc_f151ca1888000160dc6ce2e46c3438bc13f9a034200b1ec5d81b03c77606eed6_page_000001.png",
+ "is_complex": false,
+ "pred_ncols": 2,
+ "pred_nrows": 2,
+ "table_id": 0,
+ "true_ncols": 2,
+ "true_nrows": 2,
+ "timing": 0.46027388400034397
+ },
+ {
+ "TEDS": 1.0,
+ "filename": "doc_1039bce8b948fac48b0b9a42f8266700a104aca355c3681a544d65e6b136b8d5_page_000001.png",
+ "is_complex": false,
+ "pred_ncols": 5,
+ "pred_nrows": 6,
+ "table_id": 0,
+ "true_ncols": 5,
+ "true_nrows": 6,
+ "timing": 1.1963975770004254
+ },
+ {
+ "TEDS": 1.0,
+ "filename": "doc_0ec2813813e8e7496d97aa4fa15dd414aa66cae2d6f62db818d30d9856ff0439_page_000001.png",
+ "is_complex": false,
+ "pred_ncols": 4,
+ "pred_nrows": 6,
+ "table_id": 1,
+ "true_ncols": 4,
+ "true_nrows": 6,
+ "timing": 0.8767309599998043
+ },
+ {
+ "TEDS": 1.0,
+ "filename": "doc_0ce1ded16152ad18c45203c1dcba0287235b888266fbcd112396fdf7cd094fab_page_000001.png",
+ "is_complex": false,
+ "pred_ncols": 4,
+ "pred_nrows": 9,
+ "table_id": 0,
+ "true_ncols": 4,
+ "true_nrows": 9,
+ "timing": 1.1361908140006562
+ },
+ {
+ "TEDS": 1.0,
+ "filename": "doc_24ab9416df440de9d792a4034e177eb71933a6eed72d4cf1ead17d22a787702f_page_000001.png",
+ "is_complex": false,
+ "pred_ncols": 3,
+ "pred_nrows": 3,
+ "table_id": 0,
+ "true_ncols": 3,
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\ No newline at end of file
diff --git a/eval/DoclingDPBench_cells2table.json b/eval/DoclingDPBench_cells2table.json
new file mode 100644
index 0000000..c4abce8
--- /dev/null
+++ b/eval/DoclingDPBench_cells2table.json
@@ -0,0 +1,606 @@
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+}
\ No newline at end of file
diff --git a/eval/FinTabNet_TableFormer.json b/eval/FinTabNet_TableFormer.json
new file mode 100644
index 0000000..8b5be60
--- /dev/null
+++ b/eval/FinTabNet_TableFormer.json
@@ -0,0 +1,11012 @@
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diff --git a/eval/FinTabNet_cells2table.json b/eval/FinTabNet_cells2table.json
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diff --git a/eval/OmniDocBench_TableFormer.json b/eval/OmniDocBench_TableFormer.json
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diff --git a/eval/OmniDocBench_cells2table.json b/eval/OmniDocBench_cells2table.json
new file mode 100644
index 0000000..fc060a7
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diff --git a/eval/PubTabNet_TableFormer.json b/eval/PubTabNet_TableFormer.json
new file mode 100644
index 0000000..2ceb5d1
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\ No newline at end of file
diff --git a/eval/PubTabNet_cells2table.json b/eval/PubTabNet_cells2table.json
new file mode 100644
index 0000000..98c9d96
--- /dev/null
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+}
\ No newline at end of file
diff --git a/eval/PulseBench-Tab_cells2table.json b/eval/PulseBench-Tab_cells2table.json
new file mode 100644
index 0000000..bfc3893
--- /dev/null
+++ b/eval/PulseBench-Tab_cells2table.json
@@ -0,0 +1,2677 @@
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+}
\ No newline at end of file
diff --git a/eval/PulseBench-Tab_pp.json b/eval/PulseBench-Tab_pp.json
new file mode 100644
index 0000000..e8f02da
--- /dev/null
+++ b/eval/PulseBench-Tab_pp.json
@@ -0,0 +1,2677 @@
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+ "rd": 0.745855
+ },
+ {
+ "TEDS": 0.991379,
+ "TLAG": 0.629743,
+ "rd": 0.495815
+ },
+ {
+ "TEDS": 0.972477,
+ "TLAG": 0.592676,
+ "rd": 0.71973
+ },
+ {
+ "TEDS": 0.84375,
+ "TLAG": 0.142512,
+ "rd": 0.626374
+ },
+ {
+ "TEDS": 1.0,
+ "TLAG": 0.635331,
+ "rd": 0.732747
+ },
+ {
+ "TEDS": 1.0,
+ "TLAG": 0.144634,
+ "rd": 0.744921
+ },
+ {
+ "TEDS": 0.941176,
+ "TLAG": 0.0,
+ "rd": 0.592593
+ },
+ {
+ "TEDS": 0.777778,
+ "TLAG": 0.075382,
+ "rd": 0.633333
+ },
+ {
+ "TEDS": 0.961538,
+ "TLAG": 0.151874,
+ "rd": 0.791667
+ },
+ {
+ "TEDS": 0.107399,
+ "TLAG": 0.531596,
+ "rd": 0.072751
+ },
+ {
+ "TEDS": 0.6,
+ "TLAG": 1.0,
+ "rd": 0.918321
+ },
+ {
+ "TEDS": 0.914286,
+ "TLAG": 0.502116,
+ "rd": 0.833997
+ },
+ {
+ "TEDS": 0.988372,
+ "TLAG": 0.772673,
+ "rd": 0.819841
+ },
+ {
+ "TEDS": 0.9,
+ "TLAG": 0.641546,
+ "rd": 0.6875
+ },
+ {
+ "TEDS": 0.987179,
+ "TLAG": 0.001028,
+ "rd": 0.735179
+ },
+ {
+ "TEDS": 0.947368,
+ "TLAG": 0.092907,
+ "rd": 0.731187
+ },
+ {
+ "TEDS": 0.727273,
+ "TLAG": 0.118118,
+ "rd": 0.637135
+ },
+ {
+ "TEDS": 1.0,
+ "TLAG": 0.356551,
+ "rd": 1.0
+ },
+ {
+ "TEDS": 0.985294,
+ "TLAG": 1.0,
+ "rd": 0.754545
+ },
+ {
+ "TEDS": 0.783784,
+ "TLAG": 0.062124,
+ "rd": 0.566667
+ },
+ {
+ "TEDS": 1.0,
+ "TLAG": 0.362284,
+ "rd": 0.761905
+ },
+ {
+ "TEDS": 0.903226,
+ "TLAG": 0.078321,
+ "rd": 0.601976
+ },
+ {
+ "TEDS": 0.863636,
+ "TLAG": 0.36115,
+ "rd": 0.533636
+ },
+ {
+ "TEDS": 0.232558,
+ "TLAG": 0.393535,
+ "rd": 0.663008
+ },
+ {
+ "TEDS": 0.864407,
+ "TLAG": 0.252246,
+ "rd": 0.49749
+ },
+ {
+ "TEDS": 0.941176,
+ "TLAG": 0.10916,
+ "rd": 1.0
+ },
+ {
+ "TEDS": 0.933333,
+ "TLAG": 0.761302,
+ "rd": 0.654887
+ },
+ {
+ "TEDS": 0.953704,
+ "TLAG": 0.6596,
+ "rd": 0.902081
+ },
+ {
+ "TEDS": 0.963504,
+ "TLAG": 0.00044,
+ "rd": 0.488731
+ },
+ {
+ "TEDS": 0.6875,
+ "TLAG": 0.516065,
+ "rd": 0.387755
+ },
+ {
+ "TEDS": 0.896552,
+ "TLAG": 1.0,
+ "rd": 0.642857
+ },
+ {
+ "TEDS": 0.847826,
+ "TLAG": 0.222486,
+ "rd": 0.544898
+ },
+ {
+ "TEDS": 0.777778,
+ "TLAG": 0.368477,
+ "rd": 0.466667
+ },
+ {
+ "TEDS": 0.666667,
+ "TLAG": 0.273374,
+ "rd": 0.611162
+ },
+ {
+ "TEDS": 0.98,
+ "TLAG": 0.462733,
+ "rd": 0.5125
+ },
+ {
+ "TEDS": 0.625,
+ "TLAG": 0.298996,
+ "rd": 0.407407
+ },
+ {
+ "TEDS": 0.986486,
+ "TLAG": 0.035162,
+ "rd": 0.569312
+ },
+ {
+ "TEDS": 1.0,
+ "TLAG": 1.0,
+ "rd": 1.0
+ },
+ {
+ "TEDS": 0.95,
+ "TLAG": 0.800014,
+ "rd": 0.775452
+ },
+ {
+ "TEDS": 0.377358,
+ "TLAG": 0.17904,
+ "rd": 0.214844
+ },
+ {
+ "TEDS": 0.944444,
+ "TLAG": 0.183878,
+ "rd": 0.770247
+ },
+ {
+ "TEDS": 0.973684,
+ "TLAG": 0.003427,
+ "rd": 0.786458
+ },
+ {
+ "TEDS": 0.928571,
+ "TLAG": 0.000076,
+ "rd": 1.0
+ },
+ {
+ "TEDS": 0.95,
+ "TLAG": 0.560904,
+ "rd": 0.5
+ },
+ {
+ "TEDS": 0.818182,
+ "TLAG": 0.870331,
+ "rd": 0.827551
+ },
+ {
+ "TEDS": 0.941176,
+ "TLAG": 0.67514,
+ "rd": 0.592593
+ },
+ {
+ "TEDS": 0.823529,
+ "TLAG": 0.261488,
+ "rd": 0.61229
+ },
+ {
+ "TEDS": 0.857143,
+ "TLAG": 0.368937,
+ "rd": 0.920721
+ },
+ {
+ "TEDS": 0.938144,
+ "TLAG": 0.724029,
+ "rd": 0.796091
+ },
+ {
+ "TEDS": 0.928571,
+ "TLAG": 0.713557,
+ "rd": 0.625
+ },
+ {
+ "TEDS": 0.977273,
+ "TLAG": 0.248506,
+ "rd": 0.516934
+ },
+ {
+ "TEDS": 0.933333,
+ "TLAG": 0.0,
+ "rd": 0.616162
+ },
+ {
+ "TEDS": 0.820896,
+ "TLAG": 0.041294,
+ "rd": 0.538499
+ },
+ {
+ "TEDS": 1.0,
+ "TLAG": 0.500017,
+ "rd": 0.672222
+ },
+ {
+ "TEDS": 0.863636,
+ "TLAG": 0.616844,
+ "rd": 0.884008
+ },
+ {
+ "TEDS": 0.986486,
+ "TLAG": 0.784987,
+ "rd": 0.865582
+ },
+ {
+ "TEDS": 0.92126,
+ "TLAG": 0.388863,
+ "rd": 0.459625
+ },
+ {
+ "TEDS": 0.875,
+ "TLAG": 0.587407,
+ "rd": 0.519131
+ },
+ {
+ "TEDS": 0.409091,
+ "TLAG": 0.0,
+ "rd": 0.630303
+ },
+ {
+ "TEDS": 0.970588,
+ "TLAG": 0.131108,
+ "rd": 0.688392
+ },
+ {
+ "TEDS": 0.95,
+ "TLAG": 0.803116,
+ "rd": 0.715385
+ },
+ {
+ "TEDS": 0.683333,
+ "TLAG": 0.426807,
+ "rd": 0.451516
+ },
+ {
+ "TEDS": 0.827586,
+ "TLAG": 0.001653,
+ "rd": 0.803571
+ },
+ {
+ "TEDS": 0.913043,
+ "TLAG": 0.000027,
+ "rd": 0.860732
+ },
+ {
+ "TEDS": 0.727612,
+ "TLAG": 0.426807,
+ "rd": 0.410175
+ },
+ {
+ "TEDS": 0.846154,
+ "TLAG": 0.056429,
+ "rd": 0.714286
+ },
+ {
+ "TEDS": 0.9375,
+ "TLAG": 0.01629,
+ "rd": 0.718656
+ },
+ {
+ "TEDS": 0.916667,
+ "TLAG": 0.004944,
+ "rd": 0.499268
+ },
+ {
+ "TEDS": 0.388889,
+ "TLAG": 0.08,
+ "rd": 0.4
+ },
+ {
+ "TEDS": 0.9375,
+ "TLAG": 0.25925,
+ "rd": 0.718656
+ },
+ {
+ "TEDS": 0.342105,
+ "TLAG": 0.125009,
+ "rd": 0.53
+ },
+ {
+ "TEDS": 0.926829,
+ "TLAG": 0.111141,
+ "rd": 0.545994
+ },
+ {
+ "TEDS": 0.606061,
+ "TLAG": 0.0,
+ "rd": 0.418975
+ },
+ {
+ "TEDS": 0.928571,
+ "TLAG": 0.075472,
+ "rd": 0.5
+ },
+ {
+ "TEDS": 0.214286,
+ "TLAG": 0.622005,
+ "rd": 0.660326
+ },
+ {
+ "TEDS": 0.652174,
+ "TLAG": 0.642442,
+ "rd": 0.734694
+ },
+ {
+ "TEDS": 0.95,
+ "TLAG": 0.153855,
+ "rd": 0.771019
+ },
+ {
+ "TEDS": 0.836066,
+ "TLAG": 0.075992,
+ "rd": 0.819228
+ },
+ {
+ "TEDS": 0.769231,
+ "TLAG": 0.603601,
+ "rd": 0.565972
+ },
+ {
+ "TEDS": 0.692308,
+ "TLAG": 0.791036,
+ "rd": 0.714286
+ },
+ {
+ "TEDS": 1.0,
+ "TLAG": 0.189836,
+ "rd": 0.782509
+ },
+ {
+ "TEDS": 0.990741,
+ "TLAG": 1.0,
+ "rd": 0.556972
+ },
+ {
+ "TEDS": 0.283688,
+ "TLAG": 0.074967,
+ "rd": 0.468857
+ },
+ {
+ "TEDS": 0.941176,
+ "TLAG": 0.0,
+ "rd": 0.78548
+ },
+ {
+ "TEDS": 0.916667,
+ "TLAG": 0.455186,
+ "rd": 0.555556
+ },
+ {
+ "TEDS": 0.955882,
+ "TLAG": 0.137931,
+ "rd": 0.853044
+ },
+ {
+ "TEDS": 0.7,
+ "TLAG": 0.350029,
+ "rd": 0.434137
+ },
+ {
+ "TEDS": 0.645833,
+ "TLAG": 0.285997,
+ "rd": 0.640598
+ },
+ {
+ "TEDS": 0.928571,
+ "TLAG": 0.0,
+ "rd": 1.0
+ },
+ {
+ "TEDS": 0.9875,
+ "TLAG": 0.805522,
+ "rd": 0.549871
+ },
+ {
+ "TEDS": 0.692308,
+ "TLAG": 0.420386,
+ "rd": 0.714286
+ },
+ {
+ "TEDS": 0.729167,
+ "TLAG": 0.642444,
+ "rd": 0.606061
+ },
+ {
+ "TEDS": 0.722222,
+ "TLAG": 0.341442,
+ "rd": 0.75
+ },
+ {
+ "TEDS": 0.854839,
+ "TLAG": 0.571595,
+ "rd": 0.519937
+ },
+ {
+ "TEDS": 0.955882,
+ "TLAG": 0.000136,
+ "rd": 0.533477
+ },
+ {
+ "TEDS": 0.961538,
+ "TLAG": 0.480969,
+ "rd": 0.625
+ },
+ {
+ "TEDS": 0.813953,
+ "TLAG": 0.052184,
+ "rd": 0.712121
+ },
+ {
+ "TEDS": 0.961538,
+ "TLAG": 0.18853,
+ "rd": 0.8125
+ },
+ {
+ "TEDS": 0.972973,
+ "TLAG": 0.560008,
+ "rd": 0.690909
+ },
+ {
+ "TEDS": 0.915254,
+ "TLAG": 0.420386,
+ "rd": 0.572917
+ },
+ {
+ "TEDS": 0.466667,
+ "TLAG": 1.0,
+ "rd": 0.389092
+ },
+ {
+ "TEDS": 0.978417,
+ "TLAG": 0.050592,
+ "rd": 0.662783
+ },
+ {
+ "TEDS": 1.0,
+ "TLAG": 0.763876,
+ "rd": 0.725
+ },
+ {
+ "TEDS": 0.852941,
+ "TLAG": 0.67746,
+ "rd": 0.635372
+ },
+ {
+ "TEDS": 0.962025,
+ "TLAG": 0.385855,
+ "rd": 0.552934
+ },
+ {
+ "TEDS": 0.961538,
+ "TLAG": 0.144777,
+ "rd": 1.0
+ },
+ {
+ "TEDS": 0.961538,
+ "TLAG": 0.232559,
+ "rd": 0.8125
+ },
+ {
+ "TEDS": 0.848485,
+ "TLAG": 0.551845,
+ "rd": 0.418975
+ },
+ {
+ "TEDS": 1.0,
+ "TLAG": 0.006891,
+ "rd": 0.909091
+ },
+ {
+ "TEDS": 0.990196,
+ "TLAG": 1.0,
+ "rd": 0.839126
+ },
+ {
+ "TEDS": 0.107463,
+ "TLAG": 0.065051,
+ "rd": 0.454986
+ },
+ {
+ "TEDS": 0.955307,
+ "TLAG": 0.000019,
+ "rd": 0.452448
+ },
+ {
+ "TEDS": 0.756757,
+ "TLAG": 0.589645,
+ "rd": 0.490278
+ },
+ {
+ "TEDS": 0.971429,
+ "TLAG": 0.748679,
+ "rd": 0.571429
+ },
+ {
+ "TEDS": 0.990741,
+ "TLAG": 0.293259,
+ "rd": 0.558618
+ },
+ {
+ "TEDS": 0.966667,
+ "TLAG": 0.763558,
+ "rd": 0.706904
+ },
+ {
+ "TEDS": 0.95,
+ "TLAG": 0.248076,
+ "rd": 1.0
+ },
+ {
+ "TEDS": 0.545455,
+ "TLAG": 0.052717,
+ "rd": 0.454545
+ },
+ {
+ "TEDS": 0.861111,
+ "TLAG": 0.318152,
+ "rd": 0.690909
+ },
+ {
+ "TEDS": 0.982759,
+ "TLAG": 0.436966,
+ "rd": 0.472167
+ },
+ {
+ "TEDS": 0.973684,
+ "TLAG": 0.350135,
+ "rd": 0.998299
+ },
+ {
+ "TEDS": 0.723464,
+ "TLAG": 0.405084,
+ "rd": 0.372266
+ }
+ ]
+}
\ No newline at end of file
diff --git a/eval/README.md b/eval/README.md
new file mode 100644
index 0000000..5135b21
--- /dev/null
+++ b/eval/README.md
@@ -0,0 +1,47 @@
+# Evaluation
+
+We use the [Docling-eval](https://github.com/docling-project/docling-eval) evaluation framework to assess the results of the table pipeline as a stage in a complete parsing solution ([Docling](https://github.com/docling-project/docling)).
+
+## Benchmarks
+
+### DP-Bench / DoclingDPBench
+
+Dataset source: [DP-Bench](https://huggingface.co/datasets/upstage/dp-bench), [DoclingDPBench](https://huggingface.co/datasets/docling-project/docling-dpbench)
+
+Docling-eval docs: [DP-Bench](https://github.com/docling-project/docling-eval/blob/main/docs/DP-Bench_benchmarks.md), [DoclingDPBench](https://github.com/docling-project/docling-eval/blob/main/docs/Docling-DP-Bench_benchmarks.md)
+
+> The benchmark dataset is gathered from three sources: 90 samples from the Library of Congress; 90 samples from Open Educational Resources; and 20 samples from Upstage's internal documents. Together, these sources provide a broad and specialized range of information.
+
+55 tables
+
+### OmniDocBench
+
+Dataset source: [OmniDocBench](https://huggingface.co/datasets/opendatalab/OmniDocBench)
+
+Docling-eval docs: [OmniDocBench](https://github.com/docling-project/docling-eval/blob/main/docs/OmniDocBench_benchmarks.md)
+
+1651 pages
+
+### FinTabNet
+
+Dataset source: [FinTabNet](https://huggingface.co/datasets/docling-project/FinTabNet_OTSL)
+
+Docling-eval docs: [FinTabNet](https://github.com/docling-project/docling-eval/blob/main/docs/FinTabNet_benchmarks.md)
+
+113k tables
+
+### PubTabNet
+
+Dataset source: [PubTabNet](https://huggingface.co/datasets/docling-project/PubTabNet_OTSL)
+
+Docling-eval docs: [PubTabNet](https://github.com/docling-project/docling-eval/blob/main/docs/PubTabNet_benchmarks.md)
+
+388k tables
+
+### PubTables-1M
+
+Dataset source: [PubTables-1M](https://huggingface.co/datasets/docling-project/PubTables-1M_OTSL)
+
+Docling-eval docs: [PubTables-1M](https://github.com/docling-project/docling-eval/blob/main/docs/P1M_benchmarks.md)
+
+1M tables
\ No newline at end of file
diff --git a/eval/generate_charts.ipynb b/eval/generate_charts.ipynb
new file mode 100644
index 0000000..5fe9f29
--- /dev/null
+++ b/eval/generate_charts.ipynb
@@ -0,0 +1,252 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "45680db9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pydantic import BaseModel\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8db4317",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Evaluation(BaseModel):\n",
+ " TEDS: float\n",
+ " filename: str\n",
+ " is_complex: bool\n",
+ " pred_ncols: int\n",
+ " pred_nrows: int\n",
+ " table_id: int\n",
+ " true_ncols: int\n",
+ " true_nrows: int\n",
+ " timing: float\n",
+ "\n",
+ "class BenchmarkStats(BaseModel):\n",
+ " benchmark_name: str\n",
+ " provider_name: str\n",
+ " TEDS_mean: float\n",
+ " TEDS_median: float\n",
+ " TEDS_std: float\n",
+ " timings_mean: float\n",
+ " timings_std: float\n",
+ " num_evaluations: int\n",
+ " evaluations: list[Evaluation]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "868ffa1b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "benchmark_name = \"PubTabNet\"\n",
+ "provider_names = [\"cells2table\", \"TableFormer\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2227ce3b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_stats: list[BenchmarkStats] = []\n",
+ "for provider_name in provider_names:\n",
+ " with open(f\"{benchmark_name}_{provider_name}.json\", \"r\") as file:\n",
+ " stats_json = file.read()\n",
+ " all_stats.append(BenchmarkStats.model_validate_json(stats_json))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "00cc3510",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(8,5))\n",
+ "\n",
+ "for stats in all_stats:\n",
+ " teds = [e.TEDS for e in stats.evaluations]\n",
+ " plt.hist(teds, bins=20, alpha=0.6, label=stats.provider_name)\n",
+ "\n",
+ "plt.xlabel(\"TEDS Score\")\n",
+ "plt.ylabel(\"Frequency\")\n",
+ "plt.title(\"TEDS Distribution\")\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "66b9c44f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(8,5))\n",
+ "\n",
+ "for stats in all_stats:\n",
+ " sizes = [e.true_nrows * e.true_ncols for e in stats.evaluations]\n",
+ " timing = [e.timing for e in stats.evaluations]\n",
+ "\n",
+ " plt.scatter(sizes, timing, alpha=0.5, label=stats.provider_name)\n",
+ "\n",
+ "plt.xlabel(\"Table Size (number of true cells)\")\n",
+ "plt.ylabel(\"Time (s)\")\n",
+ "plt.title(\"Timing vs Table Size\")\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c482b7cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for stats in all_stats:\n",
+ " simple = [e.TEDS for e in stats.evaluations if not e.is_complex]\n",
+ " complex_ = [e.TEDS for e in stats.evaluations if e.is_complex]\n",
+ "\n",
+ " plt.boxplot([simple, complex_], tick_labels=[\"Simple\", \"Complex\"])\n",
+ " plt.ylabel(\"TEDS\")\n",
+ " plt.title(f\"Impact of Table Complexity on {stats.provider_name}\")\n",
+ " plt.grid(True)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b8ec5700",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for stats in all_stats:\n",
+ " row_error = [abs(e.pred_nrows - e.true_nrows) for e in stats.evaluations]\n",
+ " col_error = [abs(e.pred_ncols - e.true_ncols) for e in stats.evaluations]\n",
+ "\n",
+ " plt.figure(figsize=(10,4))\n",
+ "\n",
+ " plt.subplot(1,2,1)\n",
+ " plt.hist(row_error, bins=20)\n",
+ " plt.title(\"Row Prediction Error\")\n",
+ "\n",
+ " plt.subplot(1,2,2)\n",
+ " plt.hist(col_error, bins=20)\n",
+ " plt.title(\"Column Prediction Error\")\n",
+ "\n",
+ " plt.tight_layout()\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f736f8f3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for stats in all_stats:\n",
+ " sizes = [e.true_nrows * e.true_ncols for e in stats.evaluations]\n",
+ " teds = [e.TEDS for e in stats.evaluations]\n",
+ "\n",
+ " plt.scatter(sizes, teds, alpha=0.5)\n",
+ " plt.xlabel(\"Table Size (#cells)\")\n",
+ " plt.ylabel(\"TEDS\")\n",
+ " plt.title(\"TEDS vs Table Size\")\n",
+ " plt.grid(True)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6e59a948",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(8,5))\n",
+ "t =[]\n",
+ "n = []\n",
+ "for stats in all_stats:\n",
+ " t.append([e.timing for e in stats.evaluations])\n",
+ " n.append(stats.provider_name)\n",
+ "\n",
+ "plt.boxplot(t, tick_labels=n)\n",
+ "plt.ylabel(\"Time (s)\")\n",
+ "plt.title(\"Timing Comparison\")\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fee5bdc7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "te = []\n",
+ "ti = []\n",
+ "n = []\n",
+ "for stats in all_stats:\n",
+ " teds = [e.TEDS for e in stats.evaluations]\n",
+ " timing = [e.timing for e in stats.evaluations]\n",
+ " te.append(np.mean(teds))\n",
+ " ti.append(np.mean(timing))\n",
+ " n.append(stats.provider_name)\n",
+ "\n",
+ "plt.figure(figsize=(10,4))\n",
+ "\n",
+ "plt.subplot(1,2,1)\n",
+ "plt.bar(n, te)\n",
+ "plt.title(\"Average TEDS\")\n",
+ "\n",
+ "plt.subplot(1,2,2)\n",
+ "plt.bar(n, ti)\n",
+ "plt.title(\"Average Timing\")\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.14.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/eval/generate_external_stats.ipynb b/eval/generate_external_stats.ipynb
new file mode 100644
index 0000000..7cfa891
--- /dev/null
+++ b/eval/generate_external_stats.ipynb
@@ -0,0 +1,186 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "45680db9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "\n",
+ "from pydantic import BaseModel\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8db4317",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Evaluation(BaseModel):\n",
+ " TEDS: float\n",
+ " TLAG: float\n",
+ " rd: float\n",
+ "\n",
+ "class Metric(BaseModel):\n",
+ " mean: float\n",
+ " median: float\n",
+ " std: float\n",
+ " p10: float\n",
+ " p25: float\n",
+ " p75: float\n",
+ " p90: float\n",
+ " perfect_count: int\n",
+ "\n",
+ "class BenchmarkStats(BaseModel):\n",
+ " benchmark_name: str\n",
+ " provider_name: str\n",
+ " TEDS: Metric\n",
+ " TLAG: Metric\n",
+ " rd: Metric\n",
+ " num_evaluations: int\n",
+ " evaluations: list[Evaluation]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "868ffa1b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "benchmark_name = \"PulseBench-Tab\"\n",
+ "provider_name = \"pp\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "00cc3510",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(f\"../benchmarks/{benchmark_name}/{provider_name}_filtered_teds.json\") as file:\n",
+ " teds_data = json.load(file)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fae75733",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(f\"../benchmarks/{benchmark_name}/{provider_name}_filtered_tlag.json\") as file:\n",
+ " tlag_data = json.load(file)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6b80031d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(f\"../benchmarks/{benchmark_name}/{provider_name}_filtered_rd.json\") as file:\n",
+ " rd_data = json.load(file)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3c562b17",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "evaluations: list[Evaluation] = []\n",
+ "\n",
+ "for teds, tlag, rd in zip(teds_data[\"per_sample\"].values(), tlag_data[\"per_sample\"].values(), rd_data[\"per_sample\"].values()):\n",
+ " evaluations.append(Evaluation(TEDS=teds, TLAG=tlag, rd=rd))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0a74c1a4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def build_metric(data: dict) -> Metric:\n",
+ " return Metric(\n",
+ " mean=data[\"summary\"][\"mean\"],\n",
+ " median=data[\"summary\"][\"median\"],\n",
+ " std=data[\"summary\"][\"std\"],\n",
+ " p10=data[\"summary\"][\"p10\"],\n",
+ " p25=data[\"summary\"][\"p25\"],\n",
+ " p75=data[\"summary\"][\"p75\"],\n",
+ " p90=data[\"summary\"][\"p90\"],\n",
+ " perfect_count=data[\"summary\"][\"perfect_count\"],\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9031e3f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "stats = BenchmarkStats(\n",
+ " benchmark_name=benchmark_name,\n",
+ " provider_name=provider_name,\n",
+ " TEDS=build_metric(teds_data),\n",
+ " TLAG=build_metric(tlag_data),\n",
+ " rd=build_metric(rd_data),\n",
+ " num_evaluations=len(evaluations),\n",
+ " evaluations=evaluations,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9db0ad5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "stats_json = stats.model_dump_json(indent=2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7945c5ec",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(f\"{benchmark_name}_{provider_name}.json\", \"w\") as file:\n",
+ " file.write(stats_json)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.14.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/eval/generate_stats.ipynb b/eval/generate_stats.ipynb
new file mode 100644
index 0000000..afaf09d
--- /dev/null
+++ b/eval/generate_stats.ipynb
@@ -0,0 +1,191 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "45680db9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "\n",
+ "from pydantic import BaseModel\n",
+ "from pyspark.sql import SparkSession\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "a8db4317",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Evaluation(BaseModel):\n",
+ " TEDS: float\n",
+ " filename: str\n",
+ " is_complex: bool\n",
+ " pred_ncols: int\n",
+ " pred_nrows: int\n",
+ " table_id: int\n",
+ " true_ncols: int\n",
+ " true_nrows: int\n",
+ " timing: float\n",
+ "\n",
+ "class BenchmarkStats(BaseModel):\n",
+ " benchmark_name: str\n",
+ " provider_name: str\n",
+ " TEDS_mean: float\n",
+ " TEDS_median: float\n",
+ " TEDS_std: float\n",
+ " timings_mean: float\n",
+ " timings_std: float\n",
+ " num_evaluations: int\n",
+ " evaluations: list[Evaluation]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "868ffa1b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "benchmark_name = \"OmniDocBench\"\n",
+ "provider_name = \"cells2table\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "00cc3510",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(f\"../benchmarks/{benchmark_name}/{provider_name}/evaluations/evaluation_{benchmark_name}_table_structure.json\") as file:\n",
+ " data = json.load(file)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "f6048da2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "spark = SparkSession.builder.appName(\"timings\").config(\"spark.driver.memory\", \"4g\").config(\"spark.executor.memory\", \"4g\").getOrCreate()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fae75733",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = spark.read.parquet(f\"../benchmarks/{benchmark_name}/{provider_name}/test/\").select([\"document_id\", \"prediction_timings\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "3c562b17",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "evaluations: list[Evaluation] = []\n",
+ "teds: list[float] = []\n",
+ "timings: list[float] = []\n",
+ "\n",
+ "for eval in data[\"table_structure_evaluations\"]:\n",
+ " if eval[\"structure_only_evaluation\"]:\n",
+ " row = df.filter(df[\"document_id\"] == eval[\"filename\"]).first()\n",
+ " if row is None:\n",
+ " break\n",
+ "\n",
+ " eval[\"timing\"] = json.loads(row[\"prediction_timings\"])[\"table_structure\"][eval[\"table_id\"]]\n",
+ "\n",
+ " timings.append(eval[\"timing\"])\n",
+ " teds.append(eval[\"TEDS\"])\n",
+ " evaluations.append(Evaluation.model_validate(eval))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "8769a616",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "TEDS_mean = np.mean(teds)\n",
+ "TEDS_std = np.std(teds)\n",
+ "TEDS_median = np.median(teds)\n",
+ "\n",
+ "timings_mean = np.mean(timings)\n",
+ "timings_std = np.std(timings)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9031e3f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "stats = BenchmarkStats(\n",
+ " benchmark_name=benchmark_name,\n",
+ " provider_name=provider_name,\n",
+ " TEDS_mean=TEDS_mean, # ty:ignore[invalid-argument-type]\n",
+ " TEDS_std=TEDS_std, # ty:ignore[invalid-argument-type]\n",
+ " TEDS_median=TEDS_median,\n",
+ " timings_mean=timings_mean, # ty:ignore[invalid-argument-type]\n",
+ " timings_std=timings_std, # ty:ignore[invalid-argument-type]\n",
+ " num_evaluations=len(evaluations),\n",
+ " evaluations=evaluations,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "e9db0ad5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "stats_json = stats.model_dump_json(indent=2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "7945c5ec",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(f\"{benchmark_name}_{provider_name}.json\", \"w\") as file:\n",
+ " file.write(stats_json)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.14.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/eval/visualizations.ipynb b/eval/visualizations.ipynb
new file mode 100644
index 0000000..f006c0f
--- /dev/null
+++ b/eval/visualizations.ipynb
@@ -0,0 +1,221 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "c82d48d3",
+ "metadata": {},
+ "source": [
+ "# Visualizing evaluation results\n",
+ "\n",
+ "Exploring the Parquet files generated by Docling-eval"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6b647497",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "from pyspark.sql import SparkSession\n",
+ "from docling_core.types import DoclingDocument\n",
+ "from IPython.display import display\n",
+ "from docling_core.transforms.visualizer.table_visualizer import TableVisualizer\n",
+ "from cells2table.utils.eval.main import analyze_image, pil_to_cv2\n",
+ "\n",
+ "%matplotlib widget"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9af31937",
+ "metadata": {},
+ "source": [
+ "Creating a SparkSession to read Parquet files"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "37ad9aa0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "spark = SparkSession.builder.appName(\"cells2table\").getOrCreate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "75eb6f1d",
+ "metadata": {},
+ "source": [
+ "## DoclingDPBench\n",
+ "\n",
+ "Bad ground-truth annotations:\n",
+ "- Missing table (doc_eded7804127acce26e4b9e7138aa8c95d0d8886280a21148efb0ae9519941ede_page_000001.png_layout.html)\n",
+ "- Bad table structure (doc_39f32d6a01b8e434ad2fb16ff3896931153cdf4f85bca09194b71e1514711355_page_000001.png_layout.html)\n",
+ "- Missing \"PORT\" cell text (doc_bdf0e09acee7a07f7e60c6055128967b36b44564842983ad3b0f273f1c1c5914_page_000001.png_layout.html)\n",
+ "- Bad table structure (doc_4d5b682effaf9580928a7073980e3916687d168238bc96b6c17222f747eacde6_page_000001.png_layout.html)\n",
+ "- Really bad in general (doc_770c9434a3530bfb67ef32510b63e1da347dfd6c51e5db9e0b13e90c8b287895_page_000001.png_layout.html)\n",
+ "\n",
+ "Bad cells2table results:\n",
+ "- Tolerance fail (doc_bdf0e09acee7a07f7e60c6055128967b36b44564842983ad3b0f273f1c1c5914_page_000001.png_layout.html)\n",
+ "- Tolerance fail (doc_b9e5879b4dcd812e854cc06c6c3cb4f54231af28c919be63e27bef4c639551c9_page_000001.png_layout.html)\n",
+ "- row_span fail (doc_646f014ab615ac22266a43be2691010032d9fd62368737d488ffcb912cd68fd2_page_000001.png_layout.html)\n",
+ "- col_span fail (doc_c31d842c665085cb1379d441ef0b65869f29537b9252956d6105e6b4cc972311_page_000001.png_layout.html)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8b4016f1",
+ "metadata": {},
+ "source": [
+ "Reading the prediction files"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "47d626dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "benchmark_path = Path(\"../benchmarks/DoclingDPBench/cells2table/test/\")\n",
+ "df = spark.read.parquet(str(benchmark_path.resolve()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a6b86747",
+ "metadata": {},
+ "source": [
+ "Understanding the Parquet schema"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4a256814",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.printSchema(level=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6f16ad0f",
+ "metadata": {},
+ "source": [
+ "Extracting the DoclingDocument of a prediction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6d3b2b49",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The ID of the document we want to analyze\n",
+ "doc_id = \"doc_4d5b682effaf9580928a7073980e3916687d168238bc96b6c17222f747eacde6_page_000001.png\"\n",
+ "\n",
+ "row = df.filter(df[\"document_id\"] == doc_id).first()\n",
+ "if row is None:\n",
+ " raise ValueError(\"Document not found\")\n",
+ "\n",
+ "doc = DoclingDocument.model_validate_json(row[\"PredictedDocument\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "90c8f5ad",
+ "metadata": {},
+ "source": [
+ "Visualizing the prediction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7ac8b18a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "table = doc.tables[0]\n",
+ "table_img = table.get_image(doc)\n",
+ "if table_img is None:\n",
+ " raise ValueError(\"Document generated without generate_page_images=True\")\n",
+ "\n",
+ "table_visualizer = TableVisualizer()\n",
+ "pred_img = table_visualizer.get_visualization(doc=doc)[1]\n",
+ "display(pred_img)\n",
+ "\n",
+ "print(table.export_to_html(doc))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b1ab6cc8",
+ "metadata": {},
+ "source": [
+ "Running the table image with the pipeline (again) to analyze cell confidence scores\n",
+ "\n",
+ "That information is lost when building a DoclingDocument"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ec49df06",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analyze_image(pil_to_cv2(table_img))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ef6741c6",
+ "metadata": {},
+ "source": [
+ "Now run an specific detection model directly, with no classifier\n",
+ "\n",
+ "- 0: Wired\n",
+ "- 1: Wireless\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "379a339c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analyze_image(pil_to_cv2(table_img), detection_model_idx=1)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.14.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pyproject.toml b/pyproject.toml
index 3227e46..b2f06f8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -16,6 +16,7 @@ classifiers = [
]
dependencies = [
"numpy (>=2.2.0,<3.0.0)",
+ "onnxruntime (>=1.23.2,<2.0.0)",
"opencv-python (>=4.11.0.86,<5.0.0.0)",
]
@@ -26,6 +27,7 @@ Issues = "https://github.com/jspast/cells2table/issues"
[project.scripts]
cells2table = "cells2table.cli:main"
cells2table-download = "cells2table.cli:download"
+cells2table-eval = "cells2table.utils.eval.main:main"
[project.entry-points.docling]
cells2table = "cells2table.docling"
@@ -33,19 +35,27 @@ cells2table = "cells2table.docling"
[project.optional-dependencies]
docling = [
"docling (>=2.66.0,<3.0.0)",
+ "torch (>=2.11.0,<3.0.0)",
+ "torchvision (>=0.26.0,<1.0.0)",
+]
+eval = [
+ "docling-eval (>=1.0.1,<2.0.0)",
+ "docling-metrics-table>=0.12.0",
+ "ipykernel (>=7.2.0,<8.0.0)",
+ "ipympl>=0.10.0",
+ "lxml>=5.4.0",
+ "matplotlib (>=3.10.8,<4.0.0)",
+ "paddleocr[doc-parser]>=3.5.0",
+ "pillow (>=12.2.0,<13.0.0)",
+ "pyspark (>=4.1.1,<5.0.0)",
+ "python-levenshtein>=0.27.3",
+ "scipy>=1.17.1",
+ "torch (>=2.11.0,<3.0.0)",
+ "torchvision (>=0.26.0,<1.0.0)",
]
huggingface = [
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]
-onnx_cpu = [
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-]
-onnx_cuda = [
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-]
-onnx_openvino = [
- "onnxruntime-openvino (>=1.23.0,<2.0.0)",
-]
[dependency-groups]
dev = [
@@ -63,14 +73,18 @@ line-length = 100
[tool.uv]
package = true
-conflicts = [
- [
- { extra = "onnx_cpu" },
- { extra = "onnx_cuda" },
- { extra = "onnx_openvino" }
- ]
-]
+prerelease = "if-necessary-or-explicit"
[tool.uv.build-backend]
module-name = "cells2table"
module-root = ""
+
+[tool.uv.sources]
+torch = { index = "torch_cpu" }
+torchvision = { index = "torch_cpu" }
+docling-eval = { git = "https://github.com/jspast/docling-eval", branch = "main" }
+
+[[tool.uv.index]]
+name = "torch_cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
diff --git a/uv.lock b/uv.lock
index 2f753f5..646833a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,18 +2,10 @@ version = 1
revision = 3
requires-python = ">=3.12, <4.0"
resolution-markers = [
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- "python_full_version >= '3.14' and sys_platform == 'emscripten'",
- "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
- "python_full_version < '3.14' and sys_platform == 'win32'",
- "python_full_version < '3.14' and sys_platform == 'emscripten'",
- "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+ "platform_machine == 'aarch64' and sys_platform == 'linux'",
+ "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')",
+ "sys_platform == 'darwin'",
]
-conflicts = [[
- { package = "cells2table", extra = "onnx-cpu" },
- { package = "cells2table", extra = "onnx-cuda" },
- { package = "cells2table", extra = "onnx-openvino" },
-]]
[[package]]
name = "accelerate"
@@ -26,13 +18,137 @@ dependencies = [
{ name = "psutil" },
{ name = "pyyaml" },
{ name = "safetensors" },
- { name = "torch" },
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+ { name = "torch", version = "2.11.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
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