JISE


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Journal of Information Science and Engineering, Vol. 35 No. 3, pp. 555-575


Automatic Text Recognition in Natural Scene Using Neural Network Classifier with Dynamic-group-based Hybrid Particle Swarm Optimization


KUANG-HUI TANG1,2, CHUAN-KUEI HUANG1 AND CHENG-JIAN LIN3,+
1Department of Industrial Education and Technology
National Changhua University of Education
Changhua, 500 Taiwan

2Department of Electronic Engineering
3Department of Computer Science and Information Engineering
National Chin-Yi University of Technology
Taichung, 411 Taiwan
E-mail: tkhf14@ncut.edu.tw; ckhuang@cc.ncue.edu.tw; cjlin@ncut.edu.tw 


This paper presents a two-stage algorithm for automatic text detection and recognition. In the first stage, using a stroke width transform and an improved connected component, an edge analysis method detects a candidate character region. Subsequently, a text region is located by filtering and linking characters with similar font sizes and colors. For the second stage, a histogram of oriented gradient is employed as a feature descriptor, and a neural network classifier is built with dynamic-group-based hybrid particle swarm optimization (DGHPSO) for character recognition. In DGHPSO, each group’s threshold value of similarity depends on the threshold values of fitness and distance. In addition, a local search algorithm is used to improve the search for a global optimum. The proposed algorithm was experimentally validated; it outperformed a number of recently published studies in terms of the text recognition rate when tested on the ICDAR 2003 database and the Street View Text database. 


Keywords: text detection, text recognition, neural network classifier, particle swarm optimization, natural scene

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