Merge pull request #2759 from pareenaverma/content_review

Fixes for ONNX LP

Author: pareenaverma

…phics-and-gaming/onnx/01_Fundamentals.md …phics-and-gaming/onnx/01_fundamentals.mdcontent/learning-paths/mobile-graphics-and-gaming/onnx/01_Fundamentals.md renamed to content/learning-paths/mobile-graphics-and-gaming/onnx/01_fundamentals.md content/learning-paths/mobile-graphics-and-gaming/onnx/01_Fundamentals.md renamed to content/learning-paths/mobile-graphics-and-gaming/onnx/01_fundamentals.md

@@ -240,10 +240,10 @@ Confusion matrix (rows=true, cols=pred):
 Saved: artifacts/cm_fp32_counts.png
 ```
 
-![img1](Figures/01.png)
+![img1](figures/01.png)
 The confusion matrix provides more insight than a single accuracy number. Each row corresponds to the true class, and each column corresponds to the predicted class. A strong diagonal indicates correct classification. In this output, blank cells (class 0) are almost always recognized correctly, while the remaining errors occur primarily between visually similar printed digits such as 6, 8, and 9.
 
 This behavior is expected and indicates that the model has learned meaningful digit features. The remaining confusions are rare and can be addressed later through targeted augmentation or higher-resolution crops if needed.
 
 ## Summary
-With inference validated and error modes understood, the digit recognizer is now ready to be embedded into the full Sudoku image-processing pipeline, where OpenCV will be used to detect the grid, rectify perspective, segment cells, and run batched ONNX inference to reconstruct and solve complete puzzles.
+With inference validated and error modes understood, the digit recognizer is now ready to be embedded into the full Sudoku image-processing pipeline, where OpenCV will be used to detect the grid, rectify perspective, segment cells, and run batched ONNX inference to reconstruct and solve complete puzzles.

…ile-graphics-and-gaming/onnx/02_Setup.md …ile-graphics-and-gaming/onnx/02_setup.mdcontent/learning-paths/mobile-graphics-and-gaming/onnx/02_Setup.md renamed to content/learning-paths/mobile-graphics-and-gaming/onnx/02_setup.md content/learning-paths/mobile-graphics-and-gaming/onnx/02_Setup.md renamed to content/learning-paths/mobile-graphics-and-gaming/onnx/02_setup.md

@@ -436,7 +436,7 @@ You simply provide the path to a Sudoku image and the ONNX model, and the script
 
 Representational result is shown below:
 
-![img2](Figures/02.png)
+![img2](figures/02.png)
 
 ## Summary
 By completing this section, you have built a full vision-to-solution Sudoku system:
@@ -445,4 +445,4 @@ By completing this section, you have built a full vision-to-solution Sudoku syst
 3. A deterministic solver,
 4. Clear visual diagnostics at every stage.
 
-In the next step of the learning path, we will focus on optimization and deployment.
+In the next step of the learning path, we will focus on optimization and deployment.

…hics-and-gaming/onnx/03_PreparingData.md …hics-and-gaming/onnx/03_preparingdata.mdcontent/learning-paths/mobile-graphics-and-gaming/onnx/03_PreparingData.md renamed to content/learning-paths/mobile-graphics-and-gaming/onnx/03_preparingdata.md content/learning-paths/mobile-graphics-and-gaming/onnx/03_PreparingData.md renamed to content/learning-paths/mobile-graphics-and-gaming/onnx/03_preparingdata.md

@@ -27,7 +27,7 @@ We start by creating a new Android project using Android Studio. This project wi
 1. Create a new project:
 * Open Android Studio and click New Project.
 * In the Templates screen, select Phone and Tablet, then choose Empty Views Activity.
-![img3](Figures/03.png)
+![img3](figures/03.png)
 
 This template creates a minimal Android application without additional UI components, which is ideal for a focused, step-by-step integration.
 
@@ -41,7 +41,7 @@ This template creates a minimal Android application without additional UI compon
 * Minimum SDK: API 24 (Android 7.0 – Nougat). This provides wide device coverage while remaining compatible with ONNX Runtime and OpenCV.
 * Build configuration language: Kotlin DSL (build.gradle.kts). We use the Kotlin DSL for Gradle, which is now the recommended option.
 
-![img4](Figures/04.png)
+![img4](figures/04.png)
 
 * After confirming these settings, click Finish. Android Studio will create the project and generate a basic MainActivity along with the necessary Gradle files.
 
@@ -201,7 +201,7 @@ Because the image area is scrollable, the layout remains usable even on smaller
 
 When rendered, this produces a clear, vertically structured interface with a fixed control panel at the top and large input/output images underneath, as shown in the figure below.
 
-![img](Figures/05.png)
+![img](figures/05.png)
 
 At this stage, the UI is intentionally minimal. In the next step, we will connect this view to the application logic in MainActivity, load a sample Sudoku bitmap, and wire up the Load image and Solve buttons to the ONNX-based processing pipeline.
 
@@ -1035,8 +1035,8 @@ To test the app:
 
 The figures below show two representative test cases. In each example, the upper image corresponds to the original Sudoku puzzle, while the lower image shows the same puzzle with the missing digits filled in and overlaid in green. This visual comparison confirms that grid detection, digit recognition, solving, and rendering are all functioning correctly on-device.
 
-![img](Figures/06.png)
-![img](Figures/07.png)
+![img](figures/06.png)
+![img](figures/07.png)
 
 These tests demonstrate that the application is robust to perspective distortion and partial digit placement, and that the model performs reliably when deployed via ONNX Runtime on Android.
 
@@ -1056,4 +1056,4 @@ This concludes the learning path and provides a solid foundation for building, o
 ## Companion code
 You can find the companion code in these repositories:
 1. [Sudoku solver](https://github.com/dawidborycki/SudokuSolverOnnx.git)
-2. [Python scripts](https://github.com/dawidborycki/ONNX-LP.git)
+2. [Python scripts](https://github.com/dawidborycki/ONNX-LP.git)

…-graphics-and-gaming/onnx/04_Training.md …-graphics-and-gaming/onnx/04_training.mdcontent/learning-paths/mobile-graphics-and-gaming/onnx/04_Training.md renamed to content/learning-paths/mobile-graphics-and-gaming/onnx/04_training.md content/learning-paths/mobile-graphics-and-gaming/onnx/04_Training.md renamed to content/learning-paths/mobile-graphics-and-gaming/onnx/04_training.md

@@ -26,7 +26,7 @@ author: Dawid Borycki
 
 ### Tags
 skilllevels: Introductory
-subjects: Machine Learning, Edge AI
+subjects: ML
 armips:
   - Cortex-A
   - Neoverse
@@ -39,7 +39,6 @@ tools_software_languages:
   - PyTorch
   - TensorFlow
   - ONNX
-  - ONNX Runtime
   - Android
   - Android Studio
   - Kotlin