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

Abstract

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
Volume35
Issue number3
DOIs
Publication statusPublished - 2019 Jan 1

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group dynamics
Particle size analysis
neural network
Particle swarm optimization (PSO)
Classifiers
Neural networks
Character recognition
Information science
stroke
fitness
information science
Color
candidacy
Group

All Science Journal Classification (ASJC) codes

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

Cite this

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Automatic text recognition in natural scene using neural network classifier with dynamic-group-based hybrid particle swarm optimization. / Kuang-Hui, Tang; Huang, Chuan-Kuei; Cheng-Jian, Lin.

In: Journal of Information Science and Engineering, Vol. 35, No. 3, 01.01.2019, p. 555-575.

Research output: Contribution to journalArticle

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