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CASIA-onDo: A New Database for Online Handwritten Document Analysis

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Pattern Recognition (ACPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13189))

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Abstract

In this paper we introduce an online handwritten document database (CASIA-onDo), serving as a standard database for the development and evaluation of methods in the field of online handwritten document layout analysis. It consists of 2,012 documents including a total of 841,159 online strokes. The database, covering Chinese and English languages, was produced by 200 writers. Six types of contents occur in the documents, namely text, formulas, diagrams, tables, figures, and lists. The distribution of different types is close to the actual situation. Benefiting from detailed annotations, CASIA-onDo can support different tasks of layout analysis under online or offline settings. Firstly, based on the semantic level annotation, it can be used for many classification tasks such as text/non-text classification, table/non-table classification, multi-class stroke classification and so on. Secondly, based on the instance level annotation, it can be used for segmentation tasks such as text line separation and formula segmentation. Thirdly, based on the various writing styles, it can be used for handwriting recognition and writer clustering tasks. In addition, we perform preliminary experiments to provide a benchmark on this database with a state-of-the-art method. More techniques can be evaluated on this challenging database in the future.

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Notes

  1. 1.

    http://www.nlpr.ia.ac.cn/databases/CASIA-onDo/index.html.

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Acknowledgement

This work has been supported by the National Key Research and Development Program under Grand No. 2020AAA0109700 and the National Natural Science Foundation of China (NSFC) under Grant No. 61773376.

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences, Beijing, 100190, People’s Republic of China

    Yu-Ting Yang, Yan-Ming Zhang, Xiao-Long Yun, Fei Yin & Cheng-Lin Liu

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China

    Yu-Ting Yang, Xiao-Long Yun & Cheng-Lin Liu

Authors
  1. Yu-Ting Yang
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  2. Yan-Ming Zhang
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  3. Xiao-Long Yun
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  4. Fei Yin
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  5. Cheng-Lin Liu
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Corresponding author

Correspondence to Yu-Ting Yang .

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Editors and Affiliations

  1. Korea University, Seoul, Korea (Republic of)

    Christian Wallraven

  2. Nanjing University, Nanjing, China

    Qingshan Liu

  3. Osaka University, Osaka, Japan

    Hajime Nagahara

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Yang, YT., Zhang, YM., Yun, XL., Yin, F., Liu, CL. (2022). CASIA-onDo: A New Database for Online Handwritten Document Analysis. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13189. Springer, Cham. https://doi.org/10.1007/978-3-031-02444-3_13

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  • DOI: https://doi.org/10.1007/978-3-031-02444-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02443-6

  • Online ISBN: 978-3-031-02444-3

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