Sinhala Character Recognition
A machine learning application that recognizes handwritten Sinhala characters from image input. The system uses a K-Nearest Neighbors (KNN) classifier trained on preprocessed character samples, paired with a graphical interface so users can draw or upload characters and see predictions in real time. The project explores classical ML for script-specific recognition without deep learning.
Year
Features
Technologies
- ·Limited availability of labeled Sinhala character datasets
- ·Complexity of Sinhala script with diacritical marks
- ·Choosing effective features and k for similar-looking characters
- ·Making the tool usable through a clear graphical interface
- ·Collected and labeled a custom Sinhala character dataset
- ·Applied preprocessing tuned for handwritten Sinhala glyphs
- ·Tuned KNN hyperparameters (k, distance metric) on validation data
- ·Wrapped inference in a simple GUI for interactive testing
Key Features
K-Nearest Neighbors classifier for Sinhala character recognition
Handwritten character input via drawing canvas or image upload
Image preprocessing and feature extraction before classification
User-friendly graphical interface for live predictions
Configurable k parameter and model evaluation workflow
Support for Sinhala script-specific character classes
Technologies
Learnings
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Implemented KNN classification for image-based character recognition
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Built image preprocessing pipelines for handwritten input
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Learned distance metrics and k-value tuning for classifier performance
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Designed an accessible GUI for non-technical users to test the model
Highlights
KNN Classifier
Handwritten Recognition
GUI Application
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