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ICDAR 2023 Competition on RoadText Video Text Detection, Tracking and Recognition

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Document Analysis and Recognition - ICDAR 2023 (ICDAR 2023)

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Abstract

In this report, we present the final results of the ICDAR 2023 Competition on RoadText Video Text Detection, Tracking and Recognition. The RoadText challenge is based on the RoadText-1K dataset and aims to assess and enhance current methods for scene text detection, recognition, and tracking in videos. The RoadText-1K dataset contains 1000 dash cam videos with annotations for text bounding boxes and transcriptions in every frame. The competition features an end-to-end task, requiring systems to accurately detect, track, and recognize text in dash cam videos. The paper presents a comprehensive review of the submitted methods along with a detailed analysis of the results obtained by the methods. The analysis provides valuable insights into the current capabilities and limitations of video text detection, tracking, and recognition systems for dashcam videos.

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Notes

  1. 1.

    https://rrc.cvc.uab.es/?ch=25.

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Acknowledgement

This work has been supported by IHub-Data at IIIT-Hyderabad, and MeitY, and grants PDC2021-121512-I00, and PID2020-116298GB-I00 funded by MCIN/AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR.

Author information

Authors and Affiliations

  1. Center for Visual Information Technology (CVIT), IIIT Hyderabad, Hyderabad, India

    George Tom, Minesh Mathew & C.V. Jawahar

  2. Computer Vision Center (CVC), UAB, Barcelona, Spain

    Sergi Garcia-Bordils & Dimosthenis Karatzas

  3. AllRead Machine Learning Technologies, Barcelona, Spain

    Sergi Garcia-Bordils

Authors
  1. George Tom
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  2. Minesh Mathew
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  3. Sergi Garcia-Bordils
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  4. Dimosthenis Karatzas
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  5. C.V. Jawahar
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Corresponding author

Correspondence to George Tom .

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Editors and Affiliations

  1. TU Dortmund University, Dortmund, Germany

    Gernot A. Fink

  2. Adobe, College Park, MN, USA

    Rajiv Jain

  3. Osaka Metropolitan University, Osaka, Japan

    Koichi Kise

  4. Rochester Institute of Technology, Rochester, NY, USA

    Richard Zanibbi

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Tom, G., Mathew, M., Garcia-Bordils, S., Karatzas, D., Jawahar, C. (2023). ICDAR 2023 Competition on RoadText Video Text Detection, Tracking and Recognition. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14188. Springer, Cham. https://doi.org/10.1007/978-3-031-41679-8_35

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  • DOI: https://doi.org/10.1007/978-3-031-41679-8_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41678-1

  • Online ISBN: 978-3-031-41679-8

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