Open AccessA New Character Segmentation Approach for Off-Line Cursive Handwritten Words
Author(s) -
Amit Choudhary,
Rahul Rishi,
Savita Ahlawat
Publication year - 2013
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2013.05.013
Subject(s) - computer science , segmentation , cursive , artificial intelligence , character (mathematics) , scale space segmentation , image segmentation , pattern recognition (psychology) , intelligent word recognition , benchmark (surveying) , segmentation based object categorization , optical character recognition , process (computing) , key (lock) , computer vision , intelligent character recognition , image (mathematics) , character recognition , mathematics , geometry , geodesy , computer security , geography , operating system
Character Segmentation is the most crucial step for any OCR (Optical Character Recognition) System. The selection of segmentation algorithm being used is the key factor in deciding the accuracy of OCR system. If there is a good segmentation of characters, the recognition accuracy will also be high. Segmentation of words into characters becomes very difficult due to the cursive and unconstrained nature of the handwritten script. This paper proposes a new vertical segmentation algorithm in which the segmentation points are located after thinning the word image to get the stroke width of a single pixel. The knowledge of shape and geometry of English characters is used in the segmentation process to detect ligatures. The proposed segmentation approach is tested on a local benchmark database and high segmentation accuracy is found to be achieved
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