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 scenario, while ignoring extreme video text
challenges, i.e., dense and small text in various scenarios. In this paper, we
establish a video text reading benchmark, named DSText V2, 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 spotters
to track and read. 2) High-proportioned small texts, coupled with the
blurriness and distortion in the video, will bring further challenges. 3)
Various new scenarios, e.g., Game, Sports, etc. The proposed DSText V2 includes
140 video clips from 7 open scenarios, supporting three tasks, i.e., video text
detection (Task 1), video text tracking (Task 2), and end-to-end video text
spotting (Task 3). In this article, we describe detailed statistical
information of the dataset, tasks, evaluation protocols, and the results
summaries. Most importantly, a thorough investigation and analysis targeting
three unique challenges derived from our dataset are provided, aiming to
provide new insights. Moreover, we hope the benchmark will promise video text
research in the community. DSText v2 is built upon DSText v1, which was
previously introduced to organize the ICDAR 2023 competition for dense and
small video text.

该研究提出了一个名为 DSText V2 的视频文本阅读基准，着重解决了视频中密集、小字体的阅读难题，并且包含了新的场景和任务，如视频文本检测、跟踪以及端到端视频文本定位。该文章还对数据集进行了详细的统计分析，并提供了洞察和分析针对该数据集中的三个独特挑战，旨在为研究人员带来新的见解。