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Recognition-based character segmentation for multi-level writing style

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Abstract

Character segmentation is an important task in optical character recognition (OCR). The quality of any OCR system is highly dependent on character segmentation algorithm. Despite the availability of various character segmentation methods proposed to date, existing methods cannot satisfyingly segment characters belonging to some complex writing styles such as the Lanna Dhamma characters. In this paper, a new character segmentation method named graph partitioning-based character segmentation is proposed to address the problem. The proposed method can deal with multi-level writing style as well as touching and broken characters. It is considered as a generalization of existing approaches to multi-level writing style. The proposed method consists of three phases. In the first phase, a newly devised over-segmentation technique based on morphological skeleton is used to obtain redundant fragments of a word image. The fragments are then used to form a segmentation hypotheses graph. In the last phase, the hypotheses graph is partitioned into subgraphs each corresponding to a segmented character using the partitioning algorithm developed specifically for character segmentation purpose. Experimental results based on handwritten Lanna Dhamma characters datasets showed that the proposed method achieved high correct segmentation rate and outperformed existing methods for the Lanna Dhamma alphabet.

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Acknowledgements

This study was funded under the Royal Golden Jubilee Ph.D. Program by the Thailand Research Fund (Grant No. PHD/0185/2556). We would like to thank Chiang Mai University, Thailand, for financial support and collection of digital Lanna archives. We also thank National Chiao Tung University, Taiwan, for supporting this work in the Intelligent Computing Laboratory.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand

    Papangkorn Inkeaw, Jakramate Bootkrajang & Jeerayut Chaijaruwanich

  2. College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand

    Phasit Charoenkwan

  3. National Electronics and Computer Technology Center, Pathum Thani, Thailand

    Sanparith Marukatat

  4. Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan

    Shinn-Ying Ho

  5. Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan

    Shinn-Ying Ho

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  1. Papangkorn Inkeaw
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  2. Jakramate Bootkrajang
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  3. Phasit Charoenkwan
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  4. Sanparith Marukatat
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  5. Shinn-Ying Ho
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  6. Jeerayut Chaijaruwanich
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Correspondence to Jeerayut Chaijaruwanich.

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Inkeaw, P., Bootkrajang, J., Charoenkwan, P. et al. Recognition-based character segmentation for multi-level writing style. IJDAR 21, 21–39 (2018). https://doi.org/10.1007/s10032-018-0302-5

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  • DOI: https://doi.org/10.1007/s10032-018-0302-5

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