Abstract
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.
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Acknowledgments
This work is partially supported by the CMATER research laboratory of the Computer Science and Engineering Department, Jadavpur University, India, and PURSE-II and UPE-II Jadavpur University projects. Ram Sarkar is partially funded by DST grant (EMR/2016/007213).
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Sarkar, S., Ghosh, M., Chatterjee, A., Malakar, S., Sarkar, R. (2019). An Advanced Particle Swarm Optimization Based Feature Selection Method for Tri-script Handwritten Digit Recognition. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1030. Springer, Singapore. https://doi.org/10.1007/978-981-13-8578-0_7
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