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A Unified Framework for Tracking Based Text Detection and Recognition from Web Videos
IEEE Transactions on Pattern Analysis and Machine Intelligence ( IF 23.6 ) Pub Date : 2017-04-12 , DOI: 10.1109/tpami.2017.2692763
Shu Tian , Xu-Cheng Yin , Ya Su , Hong-Wei Hao

Video text extraction plays an important role for multimedia understanding and retrieval. Most previous research efforts are conducted within individual frames. A few of recent methods, which pay attention to text tracking using multiple frames, however, do not effectively mine the relations among text detection, tracking and recognition. In this paper, we propose a generic Bayesian-based framework of Tracking based Text Detection And Recognition (T $^2$DAR) from web videos for embedded captions, which is composed of three major components, i.e., text tracking, tracking based text detection, and tracking based text recognition. In this unified framework, text tracking is first conducted by tracking-by-detection. Tracking trajectories are then revised and refined with detection or recognition results. Text detection or recognition is finally improved with multi-frame integration. Moreover, a challenging video text (embedded caption text) database (USTB-VidTEXT) is constructed and publicly available. A variety of experiments on this dataset verify that our proposed approach largely improves the performance of text detection and recognition from web videos.

中文翻译:

用于从网络视频中进行基于跟踪的文本检测和识别的统一框架

视频文本提取对于多媒体的理解和检索起着重要的作用。以前的大多数研究工作都是在单个框架内进行的。然而,一些最近的方法关注使用多个帧的文本跟踪,但是不能有效地挖掘文本检测,跟踪和识别之间的关系。在本文中,我们提出了一个基于贝叶斯的通用框架,该框架基于跟踪的文本检测和识别(T$ ^ 2 $嵌入式字幕的网络视频中的DAR),它由三个主要部分组成,即文本跟踪,基于跟踪的文本检测和基于跟踪的文本识别。在此统一框架中,首先通过按检测跟踪来进行文本跟踪。然后使用检测或识别结果对跟踪轨迹进行修订和完善。最终,通过多帧集成改进了文本检测或识别。此外,构建了具有挑战性的视频文本(嵌入式字幕文本)数据库(USTB-VidTEXT),并可以公开获得。在此数据集上进行的各种实验证明,我们提出的方法在很大程度上提高了从网络视频进行文本检测和识别的性能。
更新日期:2018-02-06
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