Update README.md

README.md

@@ -1,60 +1,128 @@
 ---
 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
+  - 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
 
-This dataset contains degraded document images with OCR text and degradation parameters.
+## 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
 
-## Features
+## Degradation Parameters
 
-- **page_filename**: Filename of the degraded image
-- **pdf_filename**: Original PDF from which the image was extracted
-- **image**: The degraded document image (viewable in the Hugging Face dataset viewer)
-- **ocr_text**: Extracted OCR text from the document
-- **params**: A JSON string containing all degradation parameters used to generate the image
+The dataset includes various degradation types:
 
-## Usage
+- **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
 
-# Load the dataset
-dataset = load_dataset("racineai/ocr-pdf-degraded")
+dataset = load_dataset("racineai/ocr-pdf-degraded", split="train")
+
+# Access a sample
+sample = dataset[0]
 
-# Display an example image
-import matplotlib.pyplot as plt
-from PIL import Image
-import io
+# Get the degraded image
+image = sample["image"]
 
-# Get an image from the dataset
-image_data = dataset['train'][0]['image']['bytes']
-img = Image.open(io.BytesIO(image_data))
-plt.imshow(img)
-plt.axis('off')
-plt.show()
+# Get the ground truth OCR text
+text = sample["ocr_text"]
 
-# View the OCR text
-print(dataset['train'][0]['ocr_text'])
+# Access degradation parameters (for targeted training/evaluation)
+params = json.loads(sample["params"])
+noise_level = params["noise_level"]
 
-# Access the degradation parameters
-params = json.loads(dataset['train'][0]['params'])
-print(params)
+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