Ancient Manuscript Ocr

🔤 Ancient Manuscript OCR - CRNN Model

State-of-the-art OCR system for ancient manuscripts using CRNN architecture.

Model Description

This model performs Optical Character Recognition (OCR) on ancient manuscript images using a Convolutional Recurrent Neural Network (CRNN) architecture with CTC Loss.

Key Achievements

• 🎯 98.49% Character Recognition Accuracy
• 📊 0.61% Character Error Rate (CER)
• 📈 1.51% Word Error Rate (WER)
• ⚡ 6.44ms Average Inference Time
• 🔢 10.8M Parameters

Model Architecture

Input Image → CNN (7 layers) → BiLSTM (2 layers) → CTC Decoder → Text Output

Components:

• CNN Backbone: 7 convolutional layers [64, 128, 256, 256, 512, 512, 512 channels]
• RNN: 2-layer Bidirectional LSTM with 256 hidden units
• Decoder: CTC (Connectionist Temporal Classification)

Training Data

• Dataset: Manuscripts Language Classification Dataset
• Images: 246,658 ancient manuscript word images
• Split: 70% train, 15% validation, 15% test
• Languages: Multiple ancient scripts (Arabic, Sanskrit, Persian, Hebrew, etc.)

Usage

Installation

pip install torch torchvision pillow

Quick Start

import torch
from PIL import Image
from inference import ManuscriptOCR

# Load model
model = ManuscriptOCR(model_path='best_model.pth')

# Predict on image
text = model.predict('path/to/manuscript.jpg')
print(f"Recognized Text: {text}")

Batch Inference

# Process multiple images
images = ['manuscript1.jpg', 'manuscript2.jpg', 'manuscript3.jpg']
results = [model.predict(img) for img in images]

for img, text in zip(images, results):
    print(f"{img}: {text}")

Performance Metrics

Metric	Train	Validation	Test
Loss	0.0234	0.0187	0.0165
CER (%)	0.58	0.61	0.61
WER (%)	1.42	1.51	1.49
Accuracy (%)	98.51	98.49	98.52

Inference Performance:

• Average inference time: 6.44ms
• Throughput: ~155 images/second
• GPU Memory: ~2.1GB

Training Details

Hyperparameters

• Optimizer: Adam (lr=0.001)
• Scheduler: ReduceLROnPlateau
• Batch Size: 64
• Dropout: 0.2
• Loss Function: CTC Loss
• Hardware: NVIDIA Tesla T4 GPU

Data Augmentation

• Random rotation (±10°)
• Random brightness (±20%)
• Random contrast (±20%)
• Horizontal padding for variable widths

Limitations

• Optimized for ancient manuscripts, not modern printed text
• Best performance on images with minimum 32px height
• Performance degrades on severely damaged manuscripts
• Works best on scripts included in training data

Citation

@misc{manuscript-ocr-2025,
  author = {Shubham Patel},
  title = {Ancient Manuscript OCR using CRNN},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/cosmicshubham/ancient-manuscript-ocr}
}

License

MIT License

Contact

• Author: Shubham Patel
• GitHub: @CosmicShubham1
• Repository: ancient-manuscript-ocr

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Model ID: cosmicshubham/ancient-manuscript-ocr
Framework: PyTorch 2.0+
Created: January 2025