---
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:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65bd1f3530ed309cb8cba833/zmyfiIIHJCUGBceBAZ3oh.png)

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