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 isolates individual characters. A vertical projection histogram (X-Y cut) makes initial cuts. Then, a crucial Structural Analysis step refines these cuts, checking pixel density and connected components (CCs) to accurately segment complex, overlapping Myanmar characters.
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.