Literature DB >> 27101607

Text Detection, Tracking and Recognition in Video: A Comprehensive Survey.

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Abstract

The intelligent analysis of video data is currently in wide demand because a video is a major source of sensory data in our lives. Text is a prominent and direct source of information in video, while the recent surveys of text detection and recognition in imagery focus mainly on text extraction from scene images. Here, this paper presents a comprehensive survey of text detection, tracking, and recognition in video with three major contributions. First, a generic framework is proposed for video text extraction that uniformly describes detection, tracking, recognition, and their relations and interactions. Second, within this framework, a variety of methods, systems, and evaluation protocols of video text extraction are summarized, compared, and analyzed. Existing text tracking techniques, tracking-based detection and recognition techniques are specifically highlighted. Third, related applications, prominent challenges, and future directions for video text extraction (especially from scene videos and web videos) are also thoroughly discussed.

Year:  2016        PMID: 27101607     DOI: 10.1109/TIP.2016.2554321

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  2 in total

1.  Comparison of RetinaNet-Based Single-Target Cascading and Multi-Target Detection Models for Administrative Regions in Network Map Pictures.

Authors:  Kaixuan Du; Xianghong Che; Yong Wang; Jiping Liu; An Luo; Ruiyuan Ma; Shenghua Xu
Journal:  Sensors (Basel)       Date:  2022-10-07       Impact factor: 3.847

2.  Biomedical literature classification with a CNNs-based hybrid learning network.

Authors:  Yan Yan; Xu-Cheng Yin; Chun Yang; Sujian Li; Bo-Wen Zhang
Journal:  PLoS One       Date:  2018-07-26       Impact factor: 3.240

  2 in total

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