TY - GEN
T1 - An Advanced Particle Swarm Optimization Based Feature Selection Method for Tri-script Handwritten Digit Recognition
AU - Sarkar, Suryadipto
AU - Ghosh, Manosij
AU - Chatterjee, Agneet
AU - Malakar, Samir
AU - Sarkar, Ram
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Handwritten digit recognition is a well-studied pattern recognition problem. Most of the techniques, reported in the literature, have been concentrated on designing several feature vectors which represent the digits in a better way. But, at times, such attempt not only increases the dimensionality of extracted feature vector but also suffers from having irrelevant and/or redundant features. To address this, in the present work, a recently introduced Particle Swarm Optimization (PSO) based feature selection method has been applied with suitable modifications. For the course of this experiment, we have confined ourselves to a newly employed feature vector for handwritten digit recognition, namely DAISY feature descriptor. The proposed feature selection method is tested on three handwritten digit databases written in Bangla, Devanagari and Roman scripts. The experimental results show that significant amount of feature dimension is reduced without compromising on recognition accuracy. Comparison of the present feature selection method with two of its ancestors also reveals that the present method outperforms the others.
AB - Handwritten digit recognition is a well-studied pattern recognition problem. Most of the techniques, reported in the literature, have been concentrated on designing several feature vectors which represent the digits in a better way. But, at times, such attempt not only increases the dimensionality of extracted feature vector but also suffers from having irrelevant and/or redundant features. To address this, in the present work, a recently introduced Particle Swarm Optimization (PSO) based feature selection method has been applied with suitable modifications. For the course of this experiment, we have confined ourselves to a newly employed feature vector for handwritten digit recognition, namely DAISY feature descriptor. The proposed feature selection method is tested on three handwritten digit databases written in Bangla, Devanagari and Roman scripts. The experimental results show that significant amount of feature dimension is reduced without compromising on recognition accuracy. Comparison of the present feature selection method with two of its ancestors also reveals that the present method outperforms the others.
UR - http://www.scopus.com/inward/record.url?scp=85068661343&partnerID=8YFLogxK
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U2 - 10.1007/978-981-13-8578-0_7
DO - 10.1007/978-981-13-8578-0_7
M3 - Conference contribution
AN - SCOPUS:85068661343
SN - 9789811385773
T3 - Communications in Computer and Information Science
SP - 82
EP - 94
BT - Computational Intelligence, Communications, and Business Analytics - 2nd International Conference, CICBA 2018, Revised Selected Papers
A2 - Mukhopadhyay, Somnath
A2 - Dutta, Paramartha
A2 - Mandal, Jyotsna Kumar
A2 - Dasgupta, Kousik
PB - Springer Verlag
T2 - 2nd International Conference on Computational Intelligence, Communication, and Business Analytics, CICBA 2018
Y2 - 27 July 2018 through 28 July 2018
ER -