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

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Abstract

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.

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Acknowledgements

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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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taipei University, 151, University Rd, Sanhsia, New Taipei City, 237, Taiwan

    Victor R. L. Shen & Jhao-Yuan Jhan

  2. Department of Information Management, Chaoyang University of Technology, Taichung City, Taiwan

    Victor R. L. Shen & Jhao-Yuan Jhan

  3. Department of Electrical Engineering, National Formosa University, Huwei Township, 632, Yunlin County, Taiwan

    Gwo-Jen Chiou

  4. Department of Electronic Engineering, Ming Chi University of Technology, New Taipei City, 243, Taiwan

    Yi-Nan Lin

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  1. Victor R. L. Shen
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  2. Gwo-Jen Chiou
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  4. Jhao-Yuan Jhan
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Correspondence to Victor R. L. Shen.

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Shen, V.R.L., Chiou, GJ., Lin, YN. et al. Novel Text Recognition Based on Modified K-Clustering and Hidden Markov Models. Wireless Pers Commun 111, 1453–1474 (2020). https://doi.org/10.1007/s11277-019-06926-6

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