Sinhala Character Recognition
A focused machine-learning project for handwritten Sinhala character recognition. Images are preprocessed with OpenCV, classified with a scikit-learn K-Nearest Neighbors model, and tested through a lightweight Tkinter UI for draw-or-upload prediction — exploring classical ML for script-specific recognition without deep learning.
Year
Features
Technologies
- Limited labeled Sinhala handwriting data
- Visually similar characters and diacritics confusing nearest neighbors
- Collected and labeled a custom character set for training
- Tuned preprocessing and KNN hyperparameters on held-out samples
Key Features
KNN classification over Sinhala character classes
Draw or upload handwritten input
OpenCV preprocessing before inference
Tkinter GUI for interactive testing
Technologies
Learnings
Applied KNN to image-based character recognition with tuned k and distance metrics
Built a small preprocessing pipeline suited to handwritten Sinhala glyphs
Wrapped the model in a GUI so non-technical users can try live predictions
Highlights
KNN Classifier
Handwritten Sinhala
Tkinter GUI
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