README.md

# IDL-WDS OCR Evaluation Dataset

## Dataset Description

This dataset is a carefully curated subset of the original [pixparse/idl-wds](https://huggingface.co/datasets/pixparse/idl-wds) dataset, specifically designed for OCR evaluation and benchmarking.

### Dataset Summary

- **Source Dataset**: [pixparse/idl-wds - Industry Documents Library (IDL)](https://huggingface.co/datasets/pixparse/idl-wds)
- **Purpose**: OCR evaluation on single-page documents
- **Sample Count**: 1,000 carefully selected single-page documents
- **Selection Criteria**: Only documents with exactly 1 page in their JSON metadata
- **Format**: Organized folder structure with paired image and ground truth data

### Key Features

- **Single-Page Focus**: All documents contain exactly one page, eliminating multi-page complexity for OCR evaluation
- **High-Quality Ground Truth**: Each sample includes detailed OCR annotations with bounding boxes, polygons, and confidence scores
- **Standardized Format**: Consistent file structure across all samples
- **Ready for Evaluation**: Pre-processed and organized for immediate use in OCR benchmarking

## Dataset Structure

### File Organization

Each sample is stored in its own folder named by the document key:

```

document_key_1/
├── image.tif          # Document image in TIFF format
└── data.json          # OCR ground truth annotations
document_key_2/
├── image.tif
└── data.json

```

### Data Format

#### Image Files (`image.tif`)
- **Format**: TIFF (Tagged Image File Format)
- **Content**: Single-page document images
- **Source**: Original document pages from the IDL collection

#### Ground Truth Files (`data.json`)
The JSON schema follows the original IDL-WDS format:

```json
{
  "pages": [
    {
      "text": [
        "Line 1 of text",
        "Line 2 of text",
        "..."
      ],
      "bbox": [
        [left, top, width, height],
        [left, top, width, height],
        "..."
      ],
      "poly": [
        [
          {"X": x1, "Y": y1}, 
          {"X": x2, "Y": y2}, 
          {"X": x3, "Y": y3}, 
          {"X": x4, "Y": y4}
        ],
        "..."
      ],
      "score": [
        confidence_score_1,
        confidence_score_2,
        "..."
      ]
    }
  ]
}
```

#### Schema Details

- **`text`**: Array of text lines in reading order
- **`bbox`**: Bounding boxes in `[left, top, width, height]` format (normalized coordinates 0-1)
- **`poly`**: Polygon coordinates for each text line (4 corner points)
- **`score`**: Confidence scores from Amazon Textract OCR (0-1 range)
- **Coordinates**: All spatial coordinates are normalized relative to page dimensions

## Usage

### Loading the Dataset

```python
import json
import os
from PIL import Image

def load_sample(sample_folder):
    """Load a single sample from the dataset"""
    image_path = os.path.join(sample_folder, "image.tif")
    json_path = os.path.join(sample_folder, "data.json")
    
    # Load image
    image = Image.open(image_path)
    
    # Load ground truth
    with open(json_path, 'r', encoding='utf-8') as f:
        ground_truth = json.load(f)
    
    return image, ground_truth

# Example usage
base_dir = "idl_wds_extracted"
sample_folders = [f for f in os.listdir(base_dir) 
                  if os.path.isdir(os.path.join(base_dir, f))]

# Load first sample
image, gt = load_sample(os.path.join(base_dir, sample_folders[0]))
print(f"Image size: {image.size}")
print(f"Number of text lines: {len(gt['pages'][0]['text'])}")
```

## Dataset Statistics

- **Total Samples**: 1,000 single-page documents
- **Source Documents**: Filtered from ~19M pages in original IDL dataset
- **Document Types**: Legal documents, internal communications, reports, and other industry documents
- **Text Languages**: Primarily English
- **Time Period**: Historical industry documents (various decades)

## Licensing and Usage

This dataset inherits the licensing terms from the original IDL dataset:

- **License**: IDL-train license (see original dataset for full terms)
- **Attribution**: Please cite the original IDL and IDL-WDS datasets

### Citation

If you use this dataset, please cite the original work:

```bibtex
@dataset{idl_wds_2023,
  title={Industry Documents Library - WebDataset Format},
  author={Pablo Montalvo and Ross Wightman},
  url={https://huggingface.co/datasets/pixparse/idl-wds},
  year={2023}
}
```

## Quality and Characteristics

### Selection Process
- Documents were filtered to include only those with exactly 1 page
- Multi-page documents were excluded to ensure consistency
- All samples verified to have both image and JSON ground truth data

### Ground Truth Quality
- OCR annotations generated using Amazon Textract
- Confidence scores provided for quality assessment
- Reading order preserved through columnar detection heuristics
- Bounding boxes and polygons for spatial understanding

### Recommended Use Cases
- OCR model evaluation and benchmarking
- Text detection algorithm testing
- Document layout analysis research
- Reading order evaluation
- OCR confidence score analysis

## Data Limitations

- **Historical Bias**: Documents reflect historical industry perspectives
- **OCR Quality**: Ground truth quality depends on Amazon Textract performance
- **Document Variety**: Limited to industry document types from IDL collection
- **Single Page Only**: Multi-page document scenarios not covered
- **Language**: Primarily English language documents

## Contact and Support

- **Original Dataset**: [pixparse/idl-wds](https://huggingface.co/datasets/pixparse/idl-wds)
- **IDL Contact**: Kate Tasker, UCSF (kate.tasker@ucsf.edu)
- **Technical Contact**: Pablo Montalvo (pablo@huggingface.co)

For questions about this specific subset, please refer to the original dataset maintainers.