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Devanagari Handwritten OCR Model
Model Summary
This model recognizes handwritten Devanagari characters and digits using a CNN trained on the UCI Devanagari dataset. It supports 46 classes (36 characters + 10 digits).
Intended Use
This model is intended for academic, research, and educational applications for digitizing handwritten Hindi text. Not suitable for production OCR without further validation.
Training Data
- Dataset: Devanagari Handwritten Character Dataset (UCI)
- Total Images: ~92,000
- Image Size: 32x32, grayscale
Model Architecture
- Input: 32x32 grayscale images
- Layers: Conv2D → MaxPooling → Dropout → Flatten → Dense → Softmax
- Optimizer: Adam
- Loss: CategoricalCrossentropy
Evaluation Metrics
- Training Accuracy: 97%
- Validation Accuracy: 91%
- Loss values plotted for 20 epochs
Limitations
- Trained only on clean, centered characters
- Doesn’t handle multi-character words or real-world handwriting noise
License
MIT License
Citation
Harish Phad (2025), Devanagari OCR using CNN, Project-Based Learning 2
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