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Texture feature-based text region segmentation in social multimedia data

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Abstract

This paper proposes a method of effectively segmenting text areas that exist in images by using the texture features of various types of input images obtained in social multimedia networks with an artificial neural network. The proposed text segmentation method consists of four main steps: a step for extracting candidate text areas, a step for localizing the text areas, a step for separating the text from the background, and a step for verifying the candidate text areas. In the candidate text area extraction step, candidate blocks that have any text areas are segmented in an input image on the basis of the texture features of the candidate blocks. In the text area localization step, only strings are extracted from the candidate text blocks. In the text and background separation step, the text areas are separated from the background area in the localized text blocks. In the candidate text area verification step, an artificial neural network is used to verify whether the extracted text blocks include actual text areas and exclude non-text areas. In the experimental results, the proposed method was applied to various types of news and non-news images, and it was found that the proposed method extracted text regions more accurately than existing methods.

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Acknowledgments

This work was supported by the ICT R&D program of MSIP/IITP. [2014(R0112-14-1014), The Development of Open Platform for Service of Convergence Contents.

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Authors and Affiliations

  1. School of Software, Soongsil University, 369, Sangdo-Ro, Dongjak-Gu, Seoul, 156-743, South Korea

    Sul-Ho Kim, Kwon-Jae An & Gye-Young Kim

  2. Department of Digital Media, Anyang University, 708-113, Anyang 5-Dong, Manan-Gu, Anyang, 430-714, South Korea

    Seok-Woo Jang

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  1. Sul-Ho Kim
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  2. Kwon-Jae An
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  4. Gye-Young Kim
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Correspondence to Gye-Young Kim.

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Kim, SH., An, KJ., Jang, SW. et al. Texture feature-based text region segmentation in social multimedia data. Multimed Tools Appl 75, 12815–12829 (2016). https://doi.org/10.1007/s11042-015-3237-6

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  • DOI: https://doi.org/10.1007/s11042-015-3237-6

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