Segmentation of Offline Handwritten Bengali Script
This addresses the segmentation bottleneck for optical character recognition in Bengali, a widely spoken language, but is incremental as it builds on existing segmentation methodologies.
The paper tackles the problem of segmenting offline handwritten Bengali script, which is challenging due to characters encircling the main character, and achieves a success rate of 97.7% on a dataset with 218 ideal segmentation points.
Character segmentation has long been one of the most critical areas of optical character recognition process. Through this operation, an image of a sequence of characters, which may be connected in some cases, is decomposed into sub-images of individual alphabetic symbols. In this paper, segmentation of cursive handwritten script of world's fourth popular language, Bengali, is considered. Unlike English script, Bengali handwritten characters and its components often encircle the main character, making the conventional segmentation methodologies inapplicable. Experimental results, using the proposed segmentation technique, on sample cursive handwritten data containing 218 ideal segmentation points show a success rate of 97.7%. Further feature-analysis on these segments may lead to actual recognition of handwritten cursive Bengali script.