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Scene text extraction based on edges and support vector regression

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Abstract

This paper presents a scene text extraction technique that automatically detects and segments texts from scene images. Three text-specific features are designed over image edges with which a set of candidate text boundaries is first detected. For each detected candidate text boundary, one or more candidate characters are then extracted by using a local threshold that is estimated based on the surrounding image pixels. The real characters and words are finally identified by a support vector regression model that is trained using bags-of-words representation. The proposed technique has been evaluated over the latest ICDAR-2013 Robust Reading Competition dataset. Experiments show that it obtains superior F-measures of 78.19 % and 75.24 % (on atom level), respectively, for the scene text detection and segmentation tasks.

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Notes

  1. http://dag.cvc.uab.es/icdar2013competition/.

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Authors and Affiliations

  1. Institute for Infocomm Research, A*STAR, 1 Fusionopolis Way, #21-01 Connexis, Singapore, 138632, Singapore

    Shijian Lu, Tao Chen & Joo-Hwee Lim

  2. School of Computing, National University of Singapore, 21 Lower Kent Ridge Road, Singapore, 119077, Singapore

    Shangxuan Tian & Chew-Lim Tan

Authors
  1. Shijian Lu
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  2. Tao Chen
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  3. Shangxuan Tian
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  4. Joo-Hwee Lim
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  5. Chew-Lim Tan
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Correspondence to Shijian Lu.

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Lu, S., Chen, T., Tian, S. et al. Scene text extraction based on edges and support vector regression. IJDAR 18, 125–135 (2015). https://doi.org/10.1007/s10032-015-0237-z

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