DSText V2: A Comprehensive Video Text Spotting Dataset for Dense and
  Small Text
Weijia Wu Yiming Zhang Yefei He Luoming Zhang Zhenyu Lou Hong Zhou Xiang Bai
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.