| --- |
| dataset_info: |
| features: |
| - name: page_filename |
| dtype: string |
| - name: pdf_filename |
| dtype: string |
| - name: image |
| dtype: image |
| struct: |
| - name: bytes |
| dtype: binary |
| - name: path |
| dtype: string |
| - name: ocr_text |
| dtype: string |
| - name: params |
| dtype: string |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| language: |
| - fr |
| - en |
| tags: |
| - military |
| - defense |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # OCR-PDF-Degraded Dataset |
|
|
| ## Overview |
|
|
| This dataset contains synthetically degraded document images paired with their ground truth OCR text. It addresses a critical gap in OCR model training by providing realistic document degradations that simulate real-world conditions encountered in production environments. |
|
|
| ## Purpose |
|
|
| Most OCR models are trained on relatively clean, perfectly scanned documents. However, in real-world applications, especially in the military/defense sector, documents may be poorly scanned, photographed in suboptimal lighting conditions, or degraded due to environmental factors. This dataset aims to: |
|
|
| 1. Enable the training of more robust OCR models that can handle imperfect document inputs |
| 2. Establish a standardized benchmark for evaluating OCR performance under various degradation conditions |
| 3. Bridge the gap between lab performance and real-world deployment for document processing systems |
|
|
| ## Domain Focus |
|
|
| This first iteration focuses specifically on military/defense sector documents. These documents: |
| - Contain specialized terminology and formatting |
| - Often include tables, diagrams, and structured information |
| - May include mission-critical information where accurate OCR is essential |
| - Represent a sector where document digitization processes may not always be ideal |
|
|
| ## Dataset Creation Process |
|
|
| The dataset was created through a systematic process of degrading clean PDF documents: |
|
|
|  |
|
|
| The process includes: |
| 1. Starting with clean military/defense PDF documents |
| 2. Extracting individual pages |
| 3. Performing OCR on the clean pages to establish ground truth text |
| 4. Applying various degradation effects to simulate real-world conditions |
| 5. Recording both the degraded images and the corresponding degradation parameters |
|
|
| ## Degradation Parameters |
|
|
| The dataset includes various degradation types: |
|
|
| - **Noise**: Random pixel noise at different intensities |
| - **Lighting**: Uneven illumination effects with varying intensities and positions |
| - **Perspective**: Distortions simulating non-flat document captures |
| - **Artifacts**: Lines, spots, and other common scanner/camera artifacts |
| - **Image Quality**: Variations in blur, brightness, contrast, and JPEG compression |
|
|
| Each image in the dataset includes specific parameter values, allowing for targeted evaluation and training. |
|
|
| ## Usage Examples |
|
|
| This dataset is ideal for: |
|
|
| ```python |
| # Example: Loading and using the dataset |
| from datasets import load_dataset |
| import json |
| |
| dataset = load_dataset("racineai/ocr-pdf-degraded", split="train") |
| |
| # Access a sample |
| sample = dataset[0] |
| |
| # Get the degraded image |
| image = sample["image"] |
| |
| # Get the ground truth OCR text |
| text = sample["ocr_text"] |
| |
| # Access degradation parameters (for targeted training/evaluation) |
| params = json.loads(sample["params"]) |
| noise_level = params["noise_level"] |
| |
| print(noise_level) |
| ``` |
|
|
| ## Limitations and Future Work |
|
|
| - Current iteration focuses only on military/defense documents |
| - Further domain expansion planned for legal, medical, and financial sectors |
| - Future versions may include handwritten text degradations |
| - Working on expansion to include multi-page document context |
|
|
| ## Citation |
|
|
| If you use this dataset in your research, please cite: |
|
|
| ``` |
| @misc{racineai_ocr_pdf_degraded, |
| author = {RacineAI}, |
| title = {OCR-PDF-Degraded: Synthetically Degraded Documents for Robust OCR}, |
| year = {2025}, |
| url = {https://huggingface.co/datasets/racineai/ocr-pdf-degraded} |
| } |
| ``` |
|
|
| ## License |
|
|
| Apache 2.0 |
