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An adaptive threshold algorithm for offline Uyghur handwritten text line segmentation

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Abstract

This paper presents an effective text-line segmentation algorithm and evaluates its performance on Uyghur handwritten text document images. Projection based adaptive threshold selection mechanism is implemented to detect and segment the text lines with different valued thresholds. The robustness of the proposed algorithm is admirable that experiments on 210 Uyghur handwritten document image including 2570 text lines got correct segmentation by 97.70% precision and 99.01% recall rate and outperformed the compared classic text-line segmentation algorithm on same evaluation set. Additionally, the proposed algorithm is tested on the public handwriting dataset and get 98.05% correct segmentation rate which is robust and promising.

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Acknowledgements

This work has been supported by the National Natural Science Foundation of China (under Grant of 61462080 and 61662076) and Ph.D. Scientific Research Startup Project of Xinjiang University.

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Authors and Affiliations

  1. School of Software, Xinjiang University, Urumqi, 830046, People’s Republic of China

    Eliyas Suleyman & Palidan Tuerxun

  2. Institute of Information Science and Engineering, Xinjiang University, Urumqi, 830046, People’s Republic of China

    Askar Hamdulla & Kamil Moydin

Authors
  1. Eliyas Suleyman
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  2. Askar Hamdulla
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  3. Palidan Tuerxun
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  4. Kamil Moydin
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Correspondence to Askar Hamdulla.

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Suleyman, E., Hamdulla, A., Tuerxun, P. et al. An adaptive threshold algorithm for offline Uyghur handwritten text line segmentation. Wireless Netw 27, 3483–3495 (2021). https://doi.org/10.1007/s11276-019-02221-1

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