NDLI: Region selection in handwritten character recognition using Artificial Bee Colony Optimization

Content Provider	IEEE Xplore Digital Library
Author	Roy, A. Das, N. Sarkar, R. Basu, S. Kundu, M. Nasipuri, M.
Copyright Year	2012
Description	Author affiliation: Computer Science and Engineering Department, Jadavpur University, Kolkata-700032, India (Roy, A.; Das, N.; Sarkar, R.; Basu, S.; Kundu, M.; Nasipuri, M.)
Abstract	Detection of local regions with optimal discriminating information from a sample of handwritten character image is one of the most challenging tasks to the pattern recognition community. In order to identify such regions, the idea of Artificial Bee Colony Optimization has been utilized in the present work. The technique is evaluated to pin point the set of local regions offering optimal discriminating feature set for handwritten numeral and character recognition. Initially, 8 directional gradient features are extracted from every region of different levels of partitions created using a CG based Quad Tree partitioning approach. Then, using the present approach, at each level, sampling process is done based on support Vector Machine (SVM) in every single region. Applying the technique we have gained 33%, 14%, 9%, 19%interms of region reduction and 0.2%, 0.4%, 0%, 1.6% in terms of recognition for Arabic, Hindi, Telugu numerals and Bangla Basic character datasets respectively. Though the success rate has not improved significantly for all the datasets, sizable amount of reduction in regions has occurred for every dataset using the present technique. Thus the cost and time of feature extraction is reduced significantly without dropping the general recognition rate.
Starting Page	183
Ending Page	186
File Size	1159354
Page Count	4
File Format	PDF
ISBN	9781467318280
e-ISBN	9781467318273
DOI	10.1109/EAIT.2012.6407891
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-30
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Region sampling Handwriting recognition Feature selection Feature extraction Handwritten character recognition Character recognition Optimization Artificial bee colony optimization Genetic algorithms
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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