  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.
