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Script Identification Based on HSV Features

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Pattern Recognition (CCPR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 663))

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Abstract

Many similar shaped scripts are used all over the world today. Scripts identification with similar shaped characters is one of the difficulties in script identification field and it need to be resolved. However, there are a little report about identification of Central Asian countries and Chinese Minority scripts, which identification of similar scripts. In this paper, a multi-script database was established, which are including 2200 plain document images with different resolution in 11 scripts such as English, Chinese, Arabic, Russian, Uyghur, Mongol, Tibet, Turkish, Kyrgyzstani, Uzbekistani and Tajikistani. Then, HSV features were extracted from each whole page image and they were classified by using BP neural network classifier. After experiment in our system, it is achieved 88.14 % of average identification rate and 99.0 % of highest identification rate in our experiment with the dataset. Experimental results indicated that HSV features were effective feature for identify these scripts.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61363064, 61563052, 61163028), College Scientific Research Plan Project of Xinjiang Uyghur Autonomous Region (No. XJEDU2013I11), and Special Training Plan Project of Xinjiang Uyghur Autonomous Region’s Minority Science and Technological Talents (No. 201323121).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Xinjiang University, Urumqi, 830046, Xinjiang, China

    Buvajar Mijit, Nurbiya Yadikar, Xing-kun Han & Kurban Ubul

  2. Network and Information Center, Xinjiang University, Urumqi, 830046, Xinjiang, China

    Alimjan Aysa

Authors
  1. Buvajar Mijit
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  2. Alimjan Aysa
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  3. Nurbiya Yadikar
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  4. Xing-kun Han
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  5. Kurban Ubul
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Corresponding author

Correspondence to Kurban Ubul .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Mijit, B., Aysa, A., Yadikar, N., Han, Xk., Ubul, K. (2016). Script Identification Based on HSV Features. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_48

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  • DOI: https://doi.org/10.1007/978-981-10-3005-5_48

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3004-8

  • Online ISBN: 978-981-10-3005-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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