Automatic text recognition in natural scene using neural network classifier with dynamic-group-based hybrid particle swarm optimization

Tang Kuang-Hui, Chuan-Kuei Huang, Lin Cheng-Jian

Research output: Contribution to journalArticle


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.

Original languageEnglish
Pages (from-to)555-575
Number of pages21
JournalJournal of Information Science and Engineering
Issue number3
Publication statusPublished - 2019 Jan 1


All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Library and Information Sciences
  • Computational Theory and Mathematics

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