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Contextual Text Block Detection Towards Scene Text Understanding

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Most existing scene text detectors focus on detecting characters or words that only capture partial text messages due to missing contextual information. For a better understanding of text in scenes, it is more desired to detect contextual text blocks (CTBs) which consist of one or multiple integral text units (e.g., characters, words, or phrases) in natural reading order and transmit certain complete text messages. This paper presents contextual text detection, a new setup that detects CTBs for better understanding of texts in scenes. We formulate the new setup by a dual detection task which first detects integral text units and then groups them into a CTB. To this end, we design a novel scene text clustering technique that treats integral text units as tokens and groups them (belonging to the same CTB) into an ordered token sequence. In addition, we create two datasets SCUT-CTW-Context and ReCTS-Context to facilitate future research, where each CTB is well annotated by an ordered sequence of integral text units. Further, we introduce three metrics that measure contextual text detection in local accuracy, continuity, and global accuracy. Extensive experiments show that our method accurately detects CTBs which effectively facilitates downstream tasks such as text classification and translation. The project is available at https://sg-vilab.github.io/publication/xue2022contextual/.

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Author information

Authors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Chuhui Xue, Jiaxing Huang & Shijian Lu

  2. ByteDance Inc., Singapore, Singapore

    Chuhui Xue, Wenqing Zhang, Changhu Wang & Song Bai

Authors
  1. Chuhui Xue
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  2. Jiaxing Huang
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  3. Wenqing Zhang
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  4. Shijian Lu
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  5. Changhu Wang
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  6. Song Bai
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Corresponding author

Correspondence to Shijian Lu .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Xue, C., Huang, J., Zhang, W., Lu, S., Wang, C., Bai, S. (2022). Contextual Text Block Detection Towards Scene Text Understanding. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13688. Springer, Cham. https://doi.org/10.1007/978-3-031-19815-1_22

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