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Text Extraction Using Sparse Representation over Learning Dictionaries

  • Thanh-Ha DoEmail author
  • Thi Minh Huyen Nguyen
  • K. C. Santosh
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)

Abstract

This paper presents a new approach for text detection using sparse representation over learned dictionaries. More specifically, the K-SVD algorithm is used for constructing two dictionaries, one for the background and one for the text. Then, text detection is done by comparing the error constructions of each patch of image over two dictionaries. Results on ICDAR dataset present that proposed method is competitive related to state-of-the-art methods.

Keywords

Text extraction Sparse representation Learning dictionary 

Notes

Acknowledgements

This research is funded by the Vietnam National University, Hanoi (VNU) under project number QG.18.04.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Thanh-Ha Do
    • 1
    Email author
  • Thi Minh Huyen Nguyen
    • 1
  • K. C. Santosh
    • 2
  1. 1.Department of InformaticsVNU University of ScienceHanoiVietnam
  2. 2.Department of Computer ScienceUniversity of South DakotaVermillionUSA

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