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Video Caption Detection

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Video Text Detection

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Video contains two types of texts. The first type pertains to caption texts which are edited texts or graphics texts artificially superimposed into video and are relevant to the content of the video. The second type belongs to scene texts, which are naturally existing texts, usually embedded in objects in the video. This chapter focuses on the state-of-the-art methods developed for caption text detection in video. According to the literature, current methods can be classified into two broad categories, namely, feature-based methods and machine learning-based methods. Feature-based methods described in this chapter make use of the following features for text detection, namely, image edges by means of gradient and filters, textures by combining a variety of image textures, connected components by analyzing skeletons obtained from the image, and frequency domain features by performing Fourier transform. On the other hand, machine learning methods presented in this chapter make use of classifiers such as support vector machines, neural networks, and Bayesian classifiers.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Nanjing University, Nanjing, China

    Tong Lu

  2. Faculty of CSIT, University of Malaya, Kuala Lumpur, Malaysia

    Shivakumara Palaiahnakote

  3. National University of Singapore, Singapore, Singapore

    Chew Lim Tan

  4. Multimedia Software Engineering Research Center, City University of Hong Kong, Kowloon Tong, Hong Kong SAR

    Wenyin Liu

Authors
  1. Tong Lu
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  2. Shivakumara Palaiahnakote
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  3. Chew Lim Tan
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  4. Wenyin Liu
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© 2014 Springer-Verlag London

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Lu, T., Palaiahnakote, S., Tan, C.L., Liu, W. (2014). Video Caption Detection. In: Video Text Detection. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6515-6_3

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  • DOI: https://doi.org/10.1007/978-1-4471-6515-6_3

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6514-9

  • Online ISBN: 978-1-4471-6515-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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