Document_analysis 📝

A Python-based toolkit for document layout analysis and classification, leveraging deep learning (CNNs/Transformers) to detect and segment document elements such as text, figures, tables, headers, and footnotes.

🚀 Features

• Detects and segments layout components: titles, text blocks, images, tables, etc.
• Trains on large-scale datasets like PubLayNet.
• End-to-end pipeline: Data preparation ➜ Model training ➜ Inference ➜ Evaluation.
• Supports state-of-the-art architectures: Faster R-CNN, Cascade R-CNN, or Transformer-based detectors.
• Metrics: mAP, IoU for layout components.

🗂️ Table of Contents

1. Installation
2. Dataset
3. Usage
4. Examples
5. Project Structure
6. Configuration
7. Troubleshooting
8. License
9. Contact

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Installation

1. Clone the repo:

   git clone https://github.com/suyashsachdeva/Document_analysis.git
   cd Document_analysis

2. (Recommended) Create a virtual environment:

   ```bash
   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

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🚀 Usage

1. Data Preparation

Prepare and preprocess data:

python scripts/prepare_data.py \
  --input_dir data/publaynet/images \
  --anno_dir data/publaynet/annotations \
  --output_dir processed_data

2. Train the Model

Train a layout detection model:

python train.py \
  --data_dir processed_data \
  --model_dir models/layout_detector \
  --epochs 30 \
  --batch_size 8 \
  --lr 1e-4

3. Run Inference

Detect layouts on new PDF or image files:

python inference.py \
  --model_dir models/layout_detector \
  --input_file samples/sample_page.jpg \
  --output_file results/prediction.json

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📸 Examples

Visuals of layout detection overlaid on sample documents can be found in /results/.

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🗂️ Project Structure

Document_analysis/
├── data/
│   └── publaynet/
├── processed_data/
├── models/
│   └── layout_detector/
├── scripts/
│   ├── prepare_data.py
│   ├── train.py
│   └── inference.py
├── requirements.txt
└── README.md

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⚙️ Configuration

Customize parameters via CLI flags or config files:

• --epochs, --batch_size, --lr
• Paths: --data_dir, --model_dir, --input_file, --output_file
• Backbone model params (ResNet, Transformer)

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🛠️ Troubleshooting

• CUDA errors: Ensure CUDA toolkit and GPU drivers are installed correctly.
• Slow performance: Reduce batch size or lower backbone resolution.
• Low accuracy: Check dataset labels, augmentation pipeline, or model depth.

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📬 Contact

For queries or issues, open an issue or contact Suyash Sachdeva.

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