sinhala-character-recognition-ml

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language: si license: openmdw library_name: scikit-learn tags:

• sinhala
• character-recognition
• knn
• ocr
• sinhala-nlp datasets:
• sathiralamal/sinhala-letter-454 metrics:
• accuracy pipeline_tag: image-classification

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🇱🇰 Handwritten Sinhala Character Recognition Model

A K-Nearest Neighbors (KNN) classifier trained to recognize 454 Sinhala characters, including base characters, vowel modifications, and combined forms. This model is designed for handwritten Sinhala character recognition.

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📊 Model Details

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Dataset

This model was trained on the Sinhala Letter and Modifications Dataset.

Citation

@dataset{sathiralamal_sinhala_2020,
  author       = {Sathira L. Amal},
  title        = {Sinhala Letter and Modifications},
  year         = {2020},
  publisher    = {Kaggle},
  url          = {https://www.kaggle.com/datasets/sathiralamal/sinhala-letter-454}
}

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Usage

Installation

pip install joblib numpy opencv-python Pillow scikit-learn

Load and Predict

import joblib
import numpy as np
import cv2

# Load the model
model = joblib.load('sinahala-character-knn.sav')

# Load and preprocess your image
def preprocess_image(image_path):
    img = cv2.imread(image_path, 0)  # Grayscale
    img = cv2.resize(img, (80, 80))   # Resize to 80x80
    img = img.flatten()               # Flatten to 6400 features
    return img.reshape(1, -1)

# Make prediction
image_path = 'path/to/your/sinhala_character.jpg'
features = preprocess_image(image_path)
prediction = model.predict(features)
print(f'Predicted class: {prediction[0]}')

With Label Dictionary

label_dict = {
    1: 'අ', 2: 'ආ', 3: 'ඇ', 4: 'ඈ', 5: 'ඉ', 
    6: 'ඊ', 7: 'උ', 8: 'එ', 9: 'ඒ', 10: 'ඔ',
    11: 'ඕ', 12: 'ක', 13: 'කා', 14: 'කැ', 15: 'කෑ',
    16: 'කි', 17: 'කී', 18: 'කු', 19: 'කූ', 20: 'ක්',
    # ... full 454 mapping
}

# Map prediction to character
char = label_dict.get(prediction[0], 'Unknown')
print(f'Predicted character: {char}')

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File Structure

sinahala-character-knn/
├── sinahala-character-knn.sav      # Trained KNN model (436 MB)
├── README.md                        # Model card
└── LICENSE                          # License file

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Use Cases

• Handwritten Sinhala character recognition
• OCR systems for Sinhala text
• Educational tools for learning Sinhala
• Digitization of Sinhala manuscripts
• Research in South Asian language processing

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Performance Metrics

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Model Limitations

• Accuracy: 60% accuracy means 4 out of 10 predictions may be incorrect.
• Handwriting Variability: Works best on similar handwriting styles to training data.
• No GPU Support: KNN runs on CPU only (no GPU acceleration).
• Memory Intensive: Stores all training samples for prediction.

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Future Improvements

• Improve accuracy with Convolutional Neural Networks (CNN)
• Data augmentation for better generalization
• Real-time webcam recognition
• Web deployment using Flask/Streamlit
• Mobile app support (Android/iOS)
• Support for cursive handwriting

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Related Resources

• GitHub Repository
• Kaggle Dataset
• Scikit-learn Documentation

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License

This model is licensed under the OpenMDW License - see the LICENSE file for details.

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Acknowledgments

• Dataset: Sathira L. Amal for the Sinhala Letter 454 dataset
• Scikit-learn community
• OpenCV community
• Hugging Face for hosting

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Contact

Lasindu Viduranga - [email protected]

Model Link: https://huggingface.co/LasinduViduranga/sinhala-character-knn

GitHub Link: https://github.com/LasinduViduranga/sinhala-character-recognition-ml

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Support

If you find this model useful, please:

• Star the GitHub repository
• Like this model on Hugging Face
• Cite the dataset if you use it in research

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Made with ❤️ for the Sinhala language community

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