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。

DSText V2：针对密集细小文字的综合视频文字识别数据集

DSText V2: A Comprehensive Video Text Spotting Dataset for Dense and  Small Text

Video text detection is considered as one of the most difficult tasks in
document analysis due to the following two challenges: 1) the difficulties
caused by video scenes, i.e., motion blur, illumination changes, and occlusion;
2) the properties of text including variants of fonts, languages, orientations,
and shapes. Most existing methods attempt to enhance the performance of video
text detection by cooperating with video text tracking, but treat these two
tasks separately. In this work, we propose an end-to-end video text detection
model with online tracking to address these two challenges. Specifically, in
the detection branch, we adopt ConvLSTM to capture spatial structure
information and motion memory. In the tracking branch, we convert the tracking
problem to text instance association, and an appearance-geometry descriptor
with memory mechanism is proposed to generate robust representation of text
instances. By integrating these two branches into one trainable framework, they
can promote each other and the computational cost is significantly reduced.
Experiments on existing video text benchmarks including ICDAR2013 Video,
Minetto and YVT demonstrate that the proposed method significantly outperforms
state-of-the-art methods. Our method improves F-score by about 2 on all
datasets and it can run realtime with 24.36 fps on TITAN Xp.

本文介绍一种基于 ConvLSTM 和在线跟踪的视频文本检测方法，将检测和跟踪的两个任务结合起来，提高了检测精度，降低了计算成本，并且实验表明该方法在 ICDAR2013 Video、Minetto 和 YVT 等数据集上均有极大的优势。