A Neuro-Fuzzy System Combined with Particle Swarm Optimization for Handwritten Character Recognition

Article type: Research Article

Authors: Chang, Bae-Muu

Affiliations: Department of Information Management, Chienkuo Technology University Chang-Hua, Chang-Hua 500, Taiwan. bmchang@cc.ctu.edu.tw

Note: [] Address for correspondence: Department of Information Management, Chienkuo Technology University Chang-Hua, Chang-Hua 500, Taiwan.

Abstract: A novel character recognition method, called a Neuro-Fuzzy system combined with Particle swarm optimization for Handwritten Character Recognition (NFPHCR), is proposed in this paper. The NFPHCR method integrates Recurrent Neural Network (RNN), Fuzzy Inference System (FIS), and Particle Swarm Optimization (PSO) algorithm to recognize handwritten characters. It employs the RNN to effectively extract oriented features of handwritten characters, and then, these features are applied to create the FIS. Finally, the FIS combined with the PSO algorithm can powerfully estimate similarity ratings between the recognized character and sampling characters in the character database. Experimental results demonstrate that the NFPHCR method achieves a satisfying recognition performance and outperforms other existing methods under considerations.

Keywords: Neuro-fuzzy system, Recurrent neural network, Fuzzy inference system, Particle swarm optimization, Handwritten character recognition

DOI: 10.3233/FI-2014-1080

Journal: Fundamenta Informaticae, vol. 133, no. 4, pp. 345-366, 2014