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Isolated Handwritten Words Segmentation Techniques in Gurmukhi Script
Author(s) -
Galaxy Bansal,
Dharam Veer Sharma
Publication year - 2010
Publication title -
international journal of computer applications
Language(s) - English
Resource type - Journals
ISSN - 0975-8887
DOI - 10.5120/547-713
Subject(s) - computer science , natural language processing , artificial intelligence , segmentation , speech recognition , pattern recognition (psychology)
Segmentation of handwritten words is a challenging task primarily because of structural features of the script and varied writing styles. Handwritten words are also prone to the problem of overlapped, connected, merged and broken characters. Based on certain properties of Gurmukhi script, different zones across the height of word are detected. Segmentation accuracy of 72.6% has been achieved with the use of the algorithms for segmenting all types of words. Segmentation accuracy of 88.1% has been achieved for segmenting all types of handwritten words in Gurmukhi script. Further, different categories of overlapping and touching characters in all the three zones (upper, middle and lower zone) of handwritten words in Gurmukhi script have been identified on the basis of structural properties of Gurmukhi script. A method for segmenting overlapping characters in middle zone has been proposed.

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