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SegHist: A General Segmentation-Based Framework for Chinese Historical Document Text Line Detection

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Document Analysis and Recognition - ICDAR 2024 (ICDAR 2024)

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Abstract

Text line detection is a key task in historical document analysis facing many challenges of arbitrary-shaped text lines, dense texts, and text lines with high aspect ratios, etc. In this paper, we propose a general framework for historical document text detection (SegHist), enabling existing segmentation-based text detection methods to effectively address the challenges, especially text lines with high aspect ratios. Integrating the SegHist framework with the commonly used method DB++, we develop DB-SegHist. This approach achieves state-of-the-art (SOTA) on the IACC2022CHDAC (CHDAC), MTHv2, and competitive results on ICDAR2019HDRC Chinese (HDRC) datasets, with a significant improvement of 1.19% on the most challenging CHDAC dataset which features more text lines with high aspect ratios. Moreover, our method attains SOTA on rotated MTHv2 and rotated HDRC, demonstrating its rotational robustness.

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Notes

  1. 1.

    Here, the aspect ratio of a text line refers to the ratio of its longer side to its shorter side. Polygonal text areas, considered as deformed rectangles, have their side lengths calculated through polyline lengths.

  2. 2.

    In seg-based methods like [18], \( D = \frac{A(1-r^2)}{L} \); here, \( r^2 \) is replaced with \( r \) (where \( r < 1 \)) to achieve a greater shrinking distance.

  3. 3.

    Since TKS treats x-direction and y-direction differently, we use text aspect ratio instead of the aspect ratio to represent the ratio of the length along the text direction (y-direction for vertical text lines) to the text width (x-direction for vertical text lines). The ratio is computed on text lines rather than kernels.

  4. 4.

    Official website of the CHDAC competition: https://iacc.pazhoulab-huangpu.com/. Similar to HisDoc R-CNN, we only utilized the official dataset and did not use any data submitted by the participants.

  5. 5.

    https://tc11.cvc.uab.es/datasets/ICDAR2019HDRC_1.

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Acknowledgement

This work is supported by the projects of National Science and Technology Major Project (2021ZD0113301) and the National Natural Science Foundation of China (No. 62376012), which is also a research achievement of the Key Laboratory of Science, Technology and Standard in Press Industry (Key Laboratory of Intelligent Press Media Technology).

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Authors and Affiliations

  1. Wangxuan Institute of Computer Technology, Peking University, Beijing, China

    Xingjian Hu, Baole Wei & Liangcai Gao

  2. Department of Information Management, Peking University, Beijing, China

    Jun Wang

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  1. Xingjian Hu
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  2. Baole Wei
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Correspondence to Liangcai Gao .

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  1. Luleå Tekniska Universitet, Luleå, Sweden

    Elisa H. Barney Smith

  2. Luleå Tekniska Universitet, Luleå, Sweden

    Marcus Liwicki

  3. Tsinghua University, Beijing, China

    Liangrui Peng

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Hu, X., Wei, B., Gao, L., Wang, J. (2024). SegHist: A General Segmentation-Based Framework for Chinese Historical Document Text Line Detection. In: Barney Smith, E.H., Liwicki, M., Peng, L. (eds) Document Analysis and Recognition - ICDAR 2024. ICDAR 2024. Lecture Notes in Computer Science, vol 14806. Springer, Cham. https://doi.org/10.1007/978-3-031-70543-4_23

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