Create README.md

README.md

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+---
+license: mit
+language:
+- en
+tags:
+- table-structure-recognition
+- table-detection
+- ocr
+- document-ai
+- icdar
+---
+
+# ICDAR-2013-Logical: A Line-Level Logical Conversion of the ICDAR 2013 Table Dataset
+
+## Dataset Description
+
+This dataset is a converted and enhanced version of the **ICDAR 2013 Table Competition dataset**, specifically reformatted for modern **Table Structure Recognition (TSR)** and **OCR** tasks. 📜
+
+The primary contribution of this version is the creation of a direct link between low-level OCR output and the table's logical structure. For each table, the dataset provides:
+1.  A high-resolution **cropped PNG image** of the table region (rendered at 144 DPI).
+2.  A detailed **JSON file** that maps each detected text line's physical bounding box to its logical grid coordinates (`[row_start, row_end, col_start, col_end]`).
+
+This format is ideal for training and evaluating Document AI models that perform OCR and table understanding concurrently.
+
+---
+
+## How to Use
+
+You can load an example by pairing the images from the `cropped_images` directory with the JSON annotations in `logical_gt`.
+
+```python
+import json
+from PIL import Image
+from pathlib import Path
+
+# Assume dataset is loaded or cloned locally
+base_path = Path("./") # Path to the dataset directory
+
+# Get a list of all examples
+gt_files = list((base_path / "logical_gt").glob("*.json"))
+example_file = gt_files[0]
+
+# Load the annotation data
+with open(example_file, 'r') as f:
+    annotations = json.load(f)
+
+# Load the corresponding image
+image_path = base_path / "cropped_images" / (example_file.stem + ".png")
+image = Image.open(image_path)
+
+# Display the first annotation for the first line of text
+first_line = annotations[0]
+print(f"Text: {first_line['text']}")
+print(f"Bounding Box: {first_line['box']}")
+print(f"Logical Coordinates: {first_line['logical_coords']}")
+
+# image.show()