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Title: Study of two zone-based features for online Bengali and Devanagari character recognition
Authors: Ghosh R.
Pratim Roy, Partha
Published in: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
Abstract: This paper presents two zone-based feature extraction approaches for online handwritten character recognition of two major Indic scripts-Bengali and Devanagari. Here, each stroke of an online character is divided into a number of local zones. In the first approach, named Zone wise structural and directional features (ZSD), structural and directional features are extracted for each stroke in each of these local zones. In the second approach, named Zone wise slopes of dominant points (ZSDP), the dominant points are detected first from each stroke and next the slope angles between consecutive dominant points are calculated and features are extracted in these local zones. Next, these features are fed to SVM classifier for stroke recognition. The constituent stroke combinations of characters are matched with training data and characters are recognized accordingly. Using ZSD, the recognition performances for Bengali (9,800 test data) and Devanagari (10,000 test data) scripts are 87.48% and 85.10% and with ZSDP, the accuracies are 92.48% and 90.63% respectively. © 2015 IEEE.
Citation: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, (2015), 401- 405
Issue Date: 2015
Publisher: IEEE Computer Society
Keywords: character recognition
dominant points
Online handwriting
zone-wise features
ISBN: 9.78148E+12
ISSN: 15205363
Author Scopus IDs: 36025087600
Author Affiliations: Ghosh, R., CSE Department, National Institute of Technology Patna, India
Roy, P.P., CSE Department, Indian Institute of Technology Roorkee, India
Appears in Collections:Conference Publications [CS]

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