Ancient Manuscript Ocr

--- language: - multilingual tags: - ocr - crnn - pytorch - ancient-manuscripts - computer-vision - historical-documents license: mit datasets: - manuscripts-language-classification metrics: - character_error_rate - word_error_rate - accuracy library_name: pytorch pipeline_tag: image-to-text --- # 🔤 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](https://www.kaggle.com/datasets/adityamukati/manuscripts-language-classification) - **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 ```bash pip install torch torchvision pillow ``` ### Quick Start ```python 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 ```python # 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...