%0 Conference Proceedings
%T ICDAR 2023 Competition on RoadText Video Text Detection, Tracking and Recognition
%A George Tom
%A Minesh Mathew
%A Sergi Garcia Bordils
%A Dimosthenis Karatzas
%A CV Jawahar
%B 17th International Conference on Document Analysis and Recognition
%D 2023
%V 14188
%F George Tom2023
%O DAG
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3905), last updated on Wed, 17 Jan 2024 15:29:38 +0100
%X 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.
%U https://link.springer.com/chapter/10.1007/978-3-031-41679-8_35
%P 577–586