- Zejiang Shen! (4), Ruochen Zhangā, Melissa Dell?, Benjamin Charles Germain Lee*, Jacob CarlsonĀ®, and Weining Li> +
+ Zejiang Shen! (4), Ruochen Zhangā, Melissa Dell?, Benjamin Charles Germain Lee*, Jacob Carlson?, and Weining LiĀ®
1 Allen Institute for AI shannons@allenai.org 2 Brown University ruochen zhang@brown.edu 3 Harvard University {melissadell,jacob carlson}@fas.harvard.edu 4 University of Washington bcgl@cs.washington.edu 5 University of Waterloo w422li@uwaterloo.ca @@ -561,8 +561,8 @@
diff --git a/test_unstructured_ingest/expected-structured-output-markdown/local-single-file-with-pdf-infer-table-structure/layout-parser-paper.pdf.md b/test_unstructured_ingest/expected-structured-output-markdown/local-single-file-with-pdf-infer-table-structure/layout-parser-paper.pdf.md index 64b76d0b73..300a88aca3 100644 --- a/test_unstructured_ingest/expected-structured-output-markdown/local-single-file-with-pdf-infer-table-structure/layout-parser-paper.pdf.md +++ b/test_unstructured_ingest/expected-structured-output-markdown/local-single-file-with-pdf-infer-table-structure/layout-parser-paper.pdf.md @@ -10,7 +10,7 @@ X r a # LayoutParser: A Uniļ¬ed Toolkit for Deep Learning Based Document Image Analysis -Zejiang Shen! (4), Ruochen Zhangā, Melissa Dell?, Benjamin Charles Germain Lee*, Jacob CarlsonĀ®, and Weining Li> +Zejiang Shen! (4), Ruochen Zhangā, Melissa Dell?, Benjamin Charles Germain Lee*, Jacob Carlson?, and Weining LiĀ® 1 Allen Institute for AI shannons@allenai.org 2 Brown University ruochen zhang@brown.edu 3 Harvard University {melissadell,jacob carlson}@fas.harvard.edu 4 University of Washington bcgl@cs.washington.edu 5 University of Waterloo w422li@uwaterloo.ca Abstract. Recent advances in document image analysis (DIA) have been primarily driven by the application of neural networks. Ideally, research outcomes could be easily deployed in production and extended for further investigation. However, various factors like loosely organized codebases and sophisticated model conļ¬gurations complicate the easy reuse of im- portant innovations by a wide audience. Though there have been on-going eļ¬orts to improve reusability and simplify deep learning (DL) model development in disciplines like natural language processing and computer vision, none of them are optimized for challenges in the domain of DIA. This represents a major gap in the existing toolkit, as DIA is central to academic research across a wide range of disciplines in the social sciences and humanities. This paper introduces LayoutParser, an open-source library for streamlining the usage of DL in DIA research and applica- tions. The core LayoutParser library comes with a set of simple and intuitive interfaces for applying and customizing DL models for layout de- tection, character recognition, and many other document processing tasks. To promote extensibility, LayoutParser also incorporates a community platform for sharing both pre-trained models and full document digiti- zation pipelines. We demonstrate that LayoutParser is helpful for both lightweight and large-scale digitization pipelines in real-word use cases. The library is publicly available at https://layout-parser.github.io. Keywords: Document Image Analysis Ā· Deep Learning Ā· Layout Analysis Ā· Character Recognition Ā· Open Source library Ā· Toolkit. @@ -120,7 +120,7 @@ Additionally, it is common for historical documents to use unique fonts with di Overall, it is possible to create an intricate and highly accurate digitization pipeline for large-scale digitization using LayoutParser. The pipeline avoids specifying the complicated rules used in traditional methods, is straightforward to develop, and is robust to outliers. The DL models also generate ļ¬ne-grained results that enable creative approaches like page reorganization for OCR. 16 This measures the overlap between the detected and ground-truth characters, and the maximum is 1. 17 This measures the number of edits from the ground-truth text to the predicted text, and lower is better. -LayoutParser: A Uniļ¬ed Toolkit for DL-Based DIA +LayoutParser: A Uniļ¬ed Toolkit for DL-Based DIA 13 ra (a) Partial table at the bottom (b) Full page table (c) Partial table at the top (d) Mis-detected text line Fig.6: This lightweight table detector can identify tables (outlined in red) and cells (shaded in blue) in diļ¬erent locations on a page. In very few cases (d), it might generate minor error predictions, e.g, failing to capture the top text line of a table. 5.2 A light-weight Visual Table Extractor diff --git a/test_unstructured_ingest/expected-structured-output/local-single-file-with-pdf-infer-table-structure/layout-parser-paper.pdf.json b/test_unstructured_ingest/expected-structured-output/local-single-file-with-pdf-infer-table-structure/layout-parser-paper.pdf.json index 670dea986a..a57495339c 100644 --- a/test_unstructured_ingest/expected-structured-output/local-single-file-with-pdf-infer-table-structure/layout-parser-paper.pdf.json +++ b/test_unstructured_ingest/expected-structured-output/local-single-file-with-pdf-infer-table-structure/layout-parser-paper.pdf.json @@ -267,8 +267,8 @@ }, { "type": "NarrativeText", - "element_id": "4dfee7e352ae892814e46bb220094b0f", - "text": "Zejiang Shen! (4), Ruochen Zhangā, Melissa Dell?, Benjamin Charles Germain Lee*, Jacob CarlsonĀ®, and Weining Li>", + "element_id": "607ee712429ac9cf3540dbdc5e55e143", + "text": "Zejiang Shen! (4), Ruochen Zhangā, Melissa Dell?, Benjamin Charles Germain Lee*, Jacob Carlson?, and Weining LiĀ®", "metadata": { "is_extracted": "true", "filetype": "application/pdf", @@ -3292,8 +3292,8 @@ }, { "type": "ListItem", - "element_id": "2b7101f39954d5301166b82906202ea9", - "text": "LayoutParser: A Uniļ¬ed Toolkit for DL-Based DIA", + "element_id": "f1b03448874d9c98a0a59a20b134c513", + "text": "LayoutParser: A Uniļ¬ed Toolkit for DL-Based DIA 13", "metadata": { "is_extracted": "true", "filetype": "application/pdf", diff --git a/unstructured/__version__.py b/unstructured/__version__.py index c558e27c3c..d817f93f7e 100644 --- a/unstructured/__version__.py +++ b/unstructured/__version__.py @@ -1 +1 @@ -__version__ = "0.22.16" # pragma: no cover +__version__ = "0.22.17" # pragma: no cover diff --git a/unstructured/partition/csv.py b/unstructured/partition/csv.py index 87f74ac168..ffbf218bdc 100644 --- a/unstructured/partition/csv.py +++ b/unstructured/partition/csv.py @@ -2,6 +2,7 @@ import contextlib import csv +import io from functools import cached_property from typing import IO, Any, Iterator @@ -58,7 +59,15 @@ def partition_csv( csv.field_size_limit(CSV_FIELD_LIMIT) with ctx.open() as file: - dataframe = pd.read_csv(file, header=ctx.header, sep=ctx.delimiter, encoding=ctx.encoding) + if ctx.delimiter is None: + dataframe = ctx.single_column_dataframe(file) + else: + dataframe = pd.read_csv( + file, + header=ctx.header, + sep=ctx.delimiter, + encoding=ctx.encoding, + ) html_table = HtmlTable.from_html_text( dataframe.to_html(index=False, header=include_header, na_rep="") @@ -122,14 +131,31 @@ def delimiter(self) -> str | None: num_bytes = 65536 with self.open() as file: - # -- read whole lines, sniffer can be confused by a trailing partial line -- - data = "\n".join( - ln.decode(self._encoding or "utf-8") for ln in file.readlines(num_bytes) - ) + sample = file.read(num_bytes + 1) + + is_truncated = len(sample) > num_bytes + if is_truncated: + sample = sample[:num_bytes] + + data = sample.decode(self._encoding or "utf-8", errors="ignore") + if is_truncated and not data.endswith(("\n", "\r")): + last_newline = max(data.rfind("\n"), data.rfind("\r")) + if last_newline != -1: + data = data[:last_newline] try: return sniffer.sniff(data, delimiters=",;|").delimiter except csv.Error: + # -- `csv.Sniffer` can fail on small files with quoted delimiters. Fall back to + # -- testing candidate delimiters and accept only those that produce a consistent + # -- multi-column shape. + candidate_delimiters = (",", ";", "|") + for delimiter in candidate_delimiters: + rows = list(csv.reader(io.StringIO(data), delimiter=delimiter)) + row_lengths = [len(row) for row in rows if row] + if row_lengths and min(row_lengths) > 1 and len(set(row_lengths)) == 1: + return delimiter + # -- sniffing will fail on single-column csv as no default can be assumed -- return None @@ -143,6 +169,23 @@ def encoding(self) -> str | None: """The encoding to use for reading the file.""" return self._encoding + def single_column_dataframe(self, file: IO[bytes]) -> pd.DataFrame: + """Parse a delimiter-less CSV while still honoring CSV quoting semantics. + + These files are treated as a single logical column, so commas remain literal text, but + quoted commas, escaped quotes, and quoted multiline fields are still decoded by the CSV + parser instead of being left as raw source text. + """ + text = file.read().decode(self.encoding or "utf-8") + rows = list(csv.reader(io.StringIO(text), delimiter="\0")) + + if self.header == 0: + if not rows: + return pd.DataFrame() + return pd.DataFrame(rows[1:], columns=rows[0]) + + return pd.DataFrame(rows) + @cached_property def last_modified(self) -> str | None: """The best last-modified date available, None if no sources are available.""" diff --git a/unstructured/partition/pdf_image/ocr.py b/unstructured/partition/pdf_image/ocr.py index 9414fa8a8d..544f210c78 100644 --- a/unstructured/partition/pdf_image/ocr.py +++ b/unstructured/partition/pdf_image/ocr.py @@ -15,7 +15,11 @@ from unstructured.metrics.table.table_formats import SimpleTableCell from unstructured.partition.common.lang import tesseract_to_paddle_language from unstructured.partition.pdf_image.analysis.layout_dump import OCRLayoutDumper -from unstructured.partition.pdf_image.pdf_image_utils import convert_pdf_to_image, valid_text +from unstructured.partition.pdf_image.pdf_image_utils import ( + convert_pdf_to_image, + get_pdfium_chunk_size, + valid_text, +) from unstructured.partition.pdf_image.pdfminer_processing import ( aggregate_embedded_text_by_block, bboxes1_is_almost_subregion_of_bboxes2, @@ -76,7 +80,15 @@ def process_data_with_ocr( Returns: DocumentLayout: The merged layout information obtained after OCR processing. """ - data_bytes = data if isinstance(data, bytes) else data.read() + original_position = None + if not isinstance(data, bytes) and hasattr(data, "tell"): + original_position = data.tell() + + try: + data_bytes = data if isinstance(data, bytes) else data.read() + finally: + if original_position is not None and not isinstance(data, bytes) and hasattr(data, "seek"): + data.seek(original_position) with tempfile.TemporaryDirectory() as tmp_dir_path: tmp_file_path = os.path.join(tmp_dir_path, "tmp_file") @@ -173,29 +185,65 @@ def process_file_with_ocr( return DocumentLayout.from_pages(merged_page_layouts) else: with tempfile.TemporaryDirectory() as temp_dir: - _image_paths = convert_pdf_to_image( - filename, - dpi=pdf_image_dpi, - output_folder=temp_dir, - path_only=True, - password=password, - ) - image_paths = cast(List[str], _image_paths) - for i, image_path in enumerate(image_paths): - extracted_regions = extracted_layout[i] if i < len(extracted_layout) else None - with PILImage.open(image_path) as image: - merged_page_layout = supplement_page_layout_with_ocr( - page_layout=out_layout.pages[i], - image=image, - infer_table_structure=infer_table_structure, - ocr_agent=ocr_agent, - ocr_languages=ocr_languages, - ocr_mode=ocr_mode, - extracted_regions=extracted_regions, - ocr_layout_dumper=ocr_layout_dumper, - table_ocr_agent=table_ocr_agent, + if not out_layout.pages: + # Preserve previous behavior for invalid files and empty placeholder layouts: + # force a render attempt so PdfiumError/FileNotFoundError still surface here. + image_paths = cast( + List[str], + convert_pdf_to_image( + filename, + dpi=pdf_image_dpi, + output_folder=temp_dir, + path_only=True, + password=password, + ), + ) + if image_paths: + raise ValueError( + "OCR received an empty layout for a PDF that rendered page images." + ) + return DocumentLayout.from_pages([]) + chunk_size = get_pdfium_chunk_size() + for chunk_start in range(0, len(out_layout.pages), chunk_size): + first_page = chunk_start + 1 + last_page = min(chunk_start + chunk_size, len(out_layout.pages)) + chunk_dir = os.path.join(temp_dir, f"chunk_{first_page}_{last_page}") + os.makedirs(chunk_dir, exist_ok=True) + _image_paths = convert_pdf_to_image( + filename, + dpi=pdf_image_dpi, + output_folder=chunk_dir, + path_only=True, + first_page=first_page, + last_page=last_page, + password=password, + ) + image_paths = cast(List[str], _image_paths) + expected_pages = last_page - first_page + 1 + if len(image_paths) != expected_pages: + raise ValueError( + f"Expected {expected_pages} rendered page(s) for range " + f"{first_page}-{last_page}, got {len(image_paths)}." + ) + for page_index, image_path in enumerate(image_paths, start=chunk_start): + extracted_regions = ( + extracted_layout[page_index] + if page_index < len(extracted_layout) + else None ) - merged_page_layouts.append(merged_page_layout) + with PILImage.open(image_path) as image: + merged_page_layout = supplement_page_layout_with_ocr( + page_layout=out_layout.pages[page_index], + image=image, + infer_table_structure=infer_table_structure, + ocr_agent=ocr_agent, + ocr_languages=ocr_languages, + ocr_mode=ocr_mode, + extracted_regions=extracted_regions, + ocr_layout_dumper=ocr_layout_dumper, + table_ocr_agent=table_ocr_agent, + ) + merged_page_layouts.append(merged_page_layout) return DocumentLayout.from_pages(merged_page_layouts) except Exception as e: if os.path.isdir(filename) or os.path.isfile(filename): diff --git a/unstructured/partition/pdf_image/pdf_image_utils.py b/unstructured/partition/pdf_image/pdf_image_utils.py index 7e6e87b695..9a041316eb 100644 --- a/unstructured/partition/pdf_image/pdf_image_utils.py +++ b/unstructured/partition/pdf_image/pdf_image_utils.py @@ -8,13 +8,23 @@ from copy import deepcopy from io import BytesIO from pathlib import Path, PurePath -from typing import IO, TYPE_CHECKING, BinaryIO, Iterator, List, Optional, Tuple, Union, cast +from typing import ( + IO, + TYPE_CHECKING, + BinaryIO, + Callable, + Iterator, + List, + Optional, + Tuple, + Union, + cast, +) import cv2 import numpy as np import pdf2image from PIL import Image -from unstructured_inference.inference.layout import convert_pdf_to_image as render_pdf_to_image from unstructured.documents.elements import ElementType from unstructured.logger import logger @@ -53,12 +63,60 @@ def write_image(image: Union[Image.Image, np.ndarray], output_image_path: str): raise ValueError("Unsupported Image Type") -def convert_pdf_to_image( - filename: str, +def _get_render_pdf_to_image() -> Callable[..., Union[List[Image.Image], List[str]]]: + # Resolve at call time so runtime monkey patches cannot be bypassed. + from unstructured_inference.inference import layout as inference_layout + + return inference_layout.convert_pdf_to_image + + +def get_pdfium_chunk_size(default: int = 8) -> int: + raw_value = os.environ.get("PDFIUM_CHUNK_SIZE") + if raw_value is None: + return default + try: + return max(1, int(raw_value)) + except ValueError: + logger.warning( + "Invalid PDFIUM_CHUNK_SIZE=%r; falling back to %d.", + raw_value, + default, + ) + return default + + +def iter_chunked_page_ranges(page_numbers: List[int], chunk_size: int) -> Iterator[Tuple[int, int]]: + if not page_numbers: + return + + sorted_pages = sorted(set(page_numbers)) + chunk_start = sorted_pages[0] + chunk_end = chunk_start + chunk_len = 1 + + for page_number in sorted_pages[1:]: + is_consecutive = page_number == chunk_end + 1 + if is_consecutive and chunk_len < chunk_size: + chunk_end = page_number + chunk_len += 1 + continue + + yield (chunk_start, chunk_end) + chunk_start = page_number + chunk_end = page_number + chunk_len = 1 + + yield (chunk_start, chunk_end) + + +def _render_pdf_pages( + filename: str = "", file: Optional[Union[bytes, BinaryIO]] = None, dpi: Optional[int] = None, output_folder: Optional[Union[str, PurePath]] = None, path_only: bool = False, + first_page: Optional[int] = None, + last_page: Optional[int] = None, password: Optional[str] = None, ) -> Union[List[Image.Image], List[str]]: exactly_one(filename=filename, file=file) @@ -66,12 +124,37 @@ def convert_pdf_to_image( if dpi is None: dpi = env_config.PDF_RENDER_DPI + render_pdf_to_image = _get_render_pdf_to_image() return render_pdf_to_image( + filename=filename or None, + file=file, + dpi=dpi, + output_folder=output_folder, + path_only=path_only, + first_page=first_page, + last_page=last_page, + password=password, + ) + + +def convert_pdf_to_image( + filename: str, + file: Optional[Union[bytes, BinaryIO]] = None, + dpi: Optional[int] = None, + output_folder: Optional[Union[str, PurePath]] = None, + path_only: bool = False, + first_page: Optional[int] = None, + last_page: Optional[int] = None, + password: Optional[str] = None, +) -> Union[List[Image.Image], List[str]]: + return _render_pdf_pages( filename=filename, file=file, dpi=dpi, output_folder=output_folder, path_only=path_only, + first_page=first_page, + last_page=last_page, password=password, ) @@ -139,86 +222,130 @@ def save_elements( os.makedirs(output_dir_path, exist_ok=True) - with tempfile.TemporaryDirectory() as temp_dir: - if is_image: - if file is None: - image_paths = [filename] - else: - if isinstance(file, bytes): - file_data = file - else: - file.seek(0) - file_data = file.read() + original_position = None + if file is not None and not isinstance(file, bytes) and hasattr(file, "tell"): + original_position = file.tell() - tmp_file_path = os.path.join(temp_dir, "tmp_file") - with open(tmp_file_path, "wb") as tmp_file: - tmp_file.write(file_data) - image_paths = [tmp_file_path] - else: - _image_paths = convert_pdf_to_image( - filename, - file, - pdf_image_dpi, - output_folder=temp_dir, - path_only=True, - password=password, - ) - image_paths = cast(List[str], _image_paths) - - figure_number = 0 - for el in elements: - if el.category != element_category_to_save: - continue - - coordinates = el.metadata.coordinates - if not coordinates or not coordinates.points: - continue - - points = coordinates.points - x1, y1 = points[0] - x2, y2 = points[2] - h_padding = env_config.EXTRACT_IMAGE_BLOCK_CROP_HORIZONTAL_PAD - v_padding = env_config.EXTRACT_IMAGE_BLOCK_CROP_VERTICAL_PAD - padded_bbox = cast( - Tuple[int, int, int, int], pad_bbox((x1, y1, x2, y2), (h_padding, v_padding)) - ) - - # The page number in the metadata may have been offset - # by starting_page_number. Make sure we use the right - # value for indexing! - assert el.metadata.page_number - metadata_page_number = el.metadata.page_number - page_index = metadata_page_number - starting_page_number - - figure_number += 1 - try: - image_path = image_paths[page_index] - image = Image.open(image_path) - cropped_image = image.crop(padded_bbox) - - # PNG images with transparency need to be converted before saving - if cropped_image.mode == "RGBA": - cropped_image = cropped_image.convert("RGB") - - if extract_image_block_to_payload: - buffered = BytesIO() - cropped_image.save(buffered, format="JPEG") - img_base64 = base64.b64encode(buffered.getvalue()) - img_base64_str = img_base64.decode() - el.metadata.image_base64 = img_base64_str - el.metadata.image_mime_type = "image/jpeg" + try: + with tempfile.TemporaryDirectory() as temp_dir: + if is_image: + if file is None: + image_paths = [filename] else: - basename = "table" if el.category == ElementType.TABLE else "figure" - assert output_dir_path - output_f_path = os.path.join( - output_dir_path, - f"{basename}-{metadata_page_number}-{figure_number}.jpg", + if isinstance(file, bytes): + file_data = file + else: + file.seek(0) + file_data = file.read() + + tmp_file_path = os.path.join(temp_dir, "tmp_file") + with open(tmp_file_path, "wb") as tmp_file: + tmp_file.write(file_data) + image_paths = [tmp_file_path] + else: + render_file = file + relevant_pdf_pages = [ + el.metadata.page_number - starting_page_number + 1 + for el in elements + if el.category == element_category_to_save + and el.metadata.coordinates + and el.metadata.coordinates.points + and el.metadata.page_number is not None + ] + image_paths_by_page: dict[int, str] = {} + chunk_size = get_pdfium_chunk_size() + for first_page, last_page in iter_chunked_page_ranges( + relevant_pdf_pages, chunk_size + ): + chunk_dir = os.path.join(temp_dir, f"chunk_{first_page}_{last_page}") + os.makedirs(chunk_dir, exist_ok=True) + if render_file is not None and not isinstance(render_file, bytes): + render_file.seek(0) + _image_paths = convert_pdf_to_image( + filename, + render_file, + pdf_image_dpi, + output_folder=chunk_dir, + path_only=True, + first_page=first_page, + last_page=last_page, + password=password, + ) + chunk_image_paths = cast(List[str], _image_paths) + expected_pages = last_page - first_page + 1 + if len(chunk_image_paths) != expected_pages: + raise ValueError( + f"Expected {expected_pages} rendered page(s) for range " + f"{first_page}-{last_page}, got {len(chunk_image_paths)}." + ) + for page_number, image_path in enumerate(chunk_image_paths, start=first_page): + image_paths_by_page[page_number] = image_path + + figure_number = 0 + for el in elements: + if el.category != element_category_to_save: + continue + + coordinates = el.metadata.coordinates + if not coordinates or not coordinates.points: + continue + + points = coordinates.points + x1, y1 = points[0] + x2, y2 = points[2] + h_padding = env_config.EXTRACT_IMAGE_BLOCK_CROP_HORIZONTAL_PAD + v_padding = env_config.EXTRACT_IMAGE_BLOCK_CROP_VERTICAL_PAD + padded_bbox = cast( + Tuple[int, int, int, int], pad_bbox((x1, y1, x2, y2), (h_padding, v_padding)) + ) + + # The page number in the metadata may have been offset + # by starting_page_number. Make sure we use the right + # value for indexing! + assert el.metadata.page_number + metadata_page_number = el.metadata.page_number + page_index = metadata_page_number - starting_page_number + pdf_page_number = page_index + 1 + + figure_number += 1 + try: + image_path = ( + image_paths[page_index] + if is_image + else image_paths_by_page[pdf_page_number] + ) + image = Image.open(image_path) + cropped_image = image.crop(padded_bbox) + + # PNG images with transparency need to be converted before saving + if cropped_image.mode == "RGBA": + cropped_image = cropped_image.convert("RGB") + + if extract_image_block_to_payload: + buffered = BytesIO() + cropped_image.save(buffered, format="JPEG") + img_base64 = base64.b64encode(buffered.getvalue()) + img_base64_str = img_base64.decode() + el.metadata.image_base64 = img_base64_str + el.metadata.image_mime_type = "image/jpeg" + else: + basename = "table" if el.category == ElementType.TABLE else "figure" + assert output_dir_path + output_f_path = os.path.join( + output_dir_path, + f"{basename}-{metadata_page_number}-{figure_number}.jpg", + ) + write_image(cropped_image, output_f_path) + # add image path to element metadata + el.metadata.image_path = output_f_path + except (ValueError, IOError): + logger.warning( + "Image Extraction Error: Skipping the failed image", + exc_info=True, ) - write_image(cropped_image, output_f_path) - # add image path to element metadata - el.metadata.image_path = output_f_path - except (ValueError, IOError): - logger.warning("Image Extraction Error: Skipping the failed image", exc_info=True) + finally: + if original_position is not None and file is not None and not isinstance(file, bytes): + file.seek(original_position) def check_element_types_to_extract( @@ -405,8 +532,8 @@ def convert_pdf_to_images( total_pages = info["Pages"] for start_page in range(1, total_pages + 1, chunk_size): end_page = min(start_page + chunk_size - 1, total_pages) - chunk_images = render_pdf_to_image( - filename=filename if f_bytes is None else None, + chunk_images = _render_pdf_pages( + filename=filename if f_bytes is None else "", file=f_bytes, dpi=env_config.PDF_RENDER_DPI, first_page=start_page, diff --git a/unstructured/partition/utils/ocr_models/tesseract_ocr.py b/unstructured/partition/utils/ocr_models/tesseract_ocr.py index 886886908b..f9f2de3d4e 100644 --- a/unstructured/partition/utils/ocr_models/tesseract_ocr.py +++ b/unstructured/partition/utils/ocr_models/tesseract_ocr.py @@ -230,7 +230,8 @@ def parse_data(self, ocr_data: pd.DataFrame, zoom: float = 1) -> TextRegions: lambda text: str(text) if not isinstance(text, str) else text.strip() ).values mask = texts != "" - element_coords = ocr_data[["left", "top", "width", "height"]].values + # Pandas can return a read-only view here, so copy before mutating width/height in place. + element_coords = ocr_data[["left", "top", "width", "height"]].to_numpy(copy=True) element_coords[:, 2] += element_coords[:, 0] element_coords[:, 3] += element_coords[:, 1] element_coords = element_coords.astype(float) / zoom diff --git a/uv.lock b/uv.lock index cf177f7703..0f0b9fe7d4 100644 --- a/uv.lock +++ b/uv.lock @@ -3,14 +3,20 @@ revision = 3 requires-python = ">=3.11, <3.14" resolution-markers = [ "python_full_version >= '3.13' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'win32'", + "(python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'linux') or (python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'linux' and sys_platform != 'win32')", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'linux'", "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'win32'", - 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