Novel Text Recognition Based on Modified K-Clustering and Hidden Markov Models

  • Victor R. L. ShenEmail author
  • Gwo-Jen Chiou
  • Yi-Nan Lin
  • Jhao-Yuan Jhan


Currently, many researchers have paid more attention to identifying scene texts from the image with background interferences. This study aims to develop an App software system with text recognition on smartphones. Otsu edge detection is applied to binarize the image and to find the parameters (i.e. weights) in a K-cluster. The modified K-cluster algorithm is used to detect the text from an image. The noise in complex background is also filtered out. The detected text gradients are evaluated by histogram of gradient. Accordingly, the distribution of the detected text gradients is generated. Finally, the gradient distribution is utilized by hidden Markov models to recognize the text. The experimental results have shown that the proposed approach can successfully outperform other methods.


Text recognition Edge detection Hidden Markov model Image processing 



The authors are very grateful to the anonymous reviewers for their constructive comments which have improved the quality of this paper. Also, this work was supported by the Ministry of Science and Technology, Taiwan, under grant MOST 106- 2221- E-845- 001.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Victor R. L. Shen
    • 1
    • 2
    Email author
  • Gwo-Jen Chiou
    • 3
  • Yi-Nan Lin
    • 4
  • Jhao-Yuan Jhan
    • 1
    • 2
  1. 1.Department of Computer Science and Information EngineeringNational Taipei UniversityNew Taipei CityTaiwan
  2. 2.Department of Information ManagementChaoyang University of TechnologyTaichung CityTaiwan
  3. 3.Department of Electrical EngineeringNational Formosa UniversityHuwei TownshipTaiwan
  4. 4.Department of Electronic EngineeringMing Chi University of TechnologyNew Taipei CityTaiwan

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