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Proceedings Paper

Script identification of handwritten word images
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Paper Abstract

This paper describes a system for script identification of handwritten word images. The system is divided into two main phases, training and testing. The training phase performs a moment based feature extraction on the training word images and generates their corresponding feature vectors. The testing phase extracts moment features from a test word image and classifies it into one of the candidate script classes using information from the trained feature vectors. Experiments are reported on handwritten word images from three scripts: Latin, Devanagari and Arabic. Three different classifiers are evaluated over a dataset consisting of 12000 word images in training set and 7942word images in testing set. Results show significant strength in the approach with all the classifiers having a consistent accuracy of over 97%.

Paper Details

Date Published: 19 January 2009
PDF: 6 pages
Proc. SPIE 7247, Document Recognition and Retrieval XVI, 72470Z (19 January 2009); doi: 10.1117/12.805682
Show Author Affiliations
Anurag Bhardwaj, Univ. at Buffalo (United States)
Huaigu Cao, Univ. at Buffalo (United States)
Venu Govindaraju, Univ. at Buffalo (United States)


Published in SPIE Proceedings Vol. 7247:
Document Recognition and Retrieval XVI
Kathrin Berkner; Laurence Likforman-Sulem, Editor(s)

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