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Letter Segmentation and Modifier Detection in Printed Sinhala Signage

Optical Character Recognition (OCR) uses digital image processing concepts to extract text from images and convert them into machine-readable characters. Image segmentation is used in OCR to select sub images that register individual letters in the input image. Different approaches have attempted to effectively segment letters of different languages in both written and printed materials. In the context of Sinhala language, the alphabet consists of symbols that are complex and vary in shape and dimensions. Isolating each letter or modifier in a Sinhala text image is a challenge due to such features. This research proposes a methodology for the segmentation of printed Sinhala letters and detection of unconnected modifiers, which uses spatial arrangement of the Sinhala words and prior knowledge about the shape of Sinhala letters and modifiers. This paper also evaluates the results of the proposed algorithm and proposes further extensions to improve the goal of letter segmentation.