This is a web-based demonstration of the OCRMPD system. Upload an image of Myanmar text to see a simulation of the recognition process based on the proposed algorithms.
The system first performs Line Segmentation using a horizontal projection histogram to isolate each line of text (blue boxes). Then, for each line, it performs Character Segmentation with the vertical histogram and structural analysis logic to isolate individual characters (red boxes).
Segments from the original image will be displayed here after processing.
After segmentation, each character image is normalized. A hybrid statistical method extracts unique features using Zone Density (pixel density in 3 horizontal zones) and Projection Area (area from top/bottom/left/right profiles). This creates a feature vector for each character.
Finally, a Hierarchical Multi-class Support Vector Machine (SVM) classifier receives the feature vector. The hierarchical model efficiently navigates the large and visually similar Myanmar character set to determine the final text output.