Recently, video text detection, tracking, and recognition in natural scenes are becoming very popular in the computer vision community. However, most existing algorithms and benchmarks focus on common text cases (e.g., normal size, density) and single scenarios, while ignoring extreme video text challenges, i.e., dense and small text in various scenarios. In this competition report, we establish a video text reading benchmark, DSText, which focuses on dense and small text reading challenges in the video with various scenarios. Compared with the previous datasets, the proposed dataset mainly include three new challenges: 1) Dense video texts, a new challenge for video text spotter. 2) High-proportioned small texts. 3) Various new scenarios, e.g., Game, sports, etc. The proposed DSText includes 100 video clips from 12 open scenarios, supporting two tasks (i.e., video text tracking (Task 1) and end-to-end video text spotting (Task 2)). During the competition period (opened on 15th February 2023 and closed on 20th March 2023), a total of 24 teams participated in the three proposed tasks with around 30 valid submissions, respectively. In this article, we describe detailed statistical information of the dataset, tasks, evaluation protocols and the results summaries of the ICDAR 2023 on DSText competition. Moreover, we hope the benchmark will promise video text research in the community.

翻译：近期，自然场景中的视频文字检测、追踪与识别在计算机视觉领域备受关注。然而，现有算法与基准测试大多聚焦于常规文字场景（如标准尺寸与密度）及单一场景，忽视了极端视频文字挑战——即多场景下的密集与小文本。本竞赛报告构建了专攻密集与小文本阅读挑战的视频文字基准数据集DSText，涵盖多类场景。与以往数据集相比，该数据集主要包含三项新挑战：1）密集视频文本——对视频文字定位器的新挑战；2）高比例小文本；3）游戏、体育等多样化新场景。DSText包含来自12类开放场景的100个视频片段，支持两项任务（视频文字追踪任务1与端到端视频文字定位任务2）。竞赛期间（2023年2月15日至3月20日），共有24支队伍参与三项任务，分别提交约30份有效方案。本文详述了数据集、任务、评估协议的统计信息，并总结了ICDAR 2023 DSText竞赛结果。我们期望该基准能够推动视频文字领域的研究发展。