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Text extraction method for historical Tibetan document images based on block projections

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Abstract

Text extraction is an important initial step in digitizing the historical documents. In this paper, we present a text extraction method for historical Tibetan document images based on block projections. The task of text extraction is considered as text area detection and location problem. The images are divided equally into blocks and the blocks are filtered by the information of the categories of connected components and corner point density. By analyzing the filtered blocks’ projections, the approximate text areas can be located, and the text regions are extracted. Experiments on the dataset of historical Tibetan documents demonstrate the effectiveness of the proposed method.

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Li-juan Duan  (段立娟) & Xi-qun Zhang  (张西群)

  2. Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, Beijing University of Technology, Beijing, 100124, China

    Li-juan Duan  (段立娟)

  3. Beijing Key Laboratory of Trusted Computing, Beijing University of Technology, Beijing, 100124, China

    Xi-qun Zhang  (张西群)

  4. Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China

    Long-long Ma  (马龙龙) & Jian Wu  (吴健)

Authors
  1. Li-juan Duan  (段立娟)
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  2. Xi-qun Zhang  (张西群)
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  3. Long-long Ma  (马龙龙)
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  4. Jian Wu  (吴健)
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Corresponding author

Correspondence to Li-juan Duan  (段立娟).

Additional information

This work has been supported by the Innovation Platform Construction of Qinghai Province (No.2016-ZJ-Y04), and the Basic Research Program of Qinghai Province (No.2016-ZJ-740). This paper was presented in part at the CCF Chinese Conference on Computer Vision, Tianjin, 2017. This paper was recommended by the program committee.

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Duan, Lj., Zhang, Xq., Ma, Ll. et al. Text extraction method for historical Tibetan document images based on block projections. Optoelectron. Lett. 13, 457–461 (2017). https://doi.org/10.1007/s11801-017-7197-0

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  • DOI: https://doi.org/10.1007/s11801-017-7197-0

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