Segmentation of Offline Handwritten Bengali Script
Subhadip Basu, Chitrita Chaudhuri, Mahantapas Kundu, Mita Nasipuri,, Dipak K. Basu

TL;DR
This paper presents a novel segmentation technique for cursive handwritten Bengali script, achieving a 97.7% success rate in decomposing connected characters into individual symbols, addressing challenges unique to Bengali handwriting.
Contribution
The paper introduces a new segmentation method tailored for Bengali cursive handwriting, overcoming limitations of traditional approaches used for Latin scripts.
Findings
97.7% segmentation success rate on sample data
Effective handling of Bengali script's encircling characters
Potential for improved handwritten Bengali recognition
Abstract
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.
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Vehicle License Plate Recognition · Image Processing and 3D Reconstruction
