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International Conference on Neural Information Processing

ICONIP 2017: Neural Information Processing pp 291–299Cite as

End-to-End Scene Text Recognition with Character Centroid Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10636))

Abstract

Scene text recognition tries to extract text information from natural images, being widely applied in computer vision and intelligent information processing. In this paper, we propose a novel end-to-end approach to scene text recognition with a specially trained fully convolutional network for predicting the centroid and pixel cluster of each character. With the help of this new information, we can solve the character instance segmentation problem effectively and then combine the recognized characters into words to accomplish the text recognition task. It is demonstrated by the experimental results on ICDAR2013 dataset that our proposed method with character centroid prediction can get a promising result on scene text recognition.

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Acknowledgments

This work was supported by the Natural Science Foundation of China under Grant 61171138.

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Authors and Affiliations

  1. Department of Information Science, School of Mathematical Sciences And LMAM, Peking University, Beijing, 100871, China

    Wei Zhao & Jinwen Ma

Authors
  1. Wei Zhao
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  2. Jinwen Ma
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Corresponding author

Correspondence to Jinwen Ma .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Cite this paper

Zhao, W., Ma, J. (2017). End-to-End Scene Text Recognition with Character Centroid Prediction. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_30

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  • DOI: https://doi.org/10.1007/978-3-319-70090-8_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70089-2

  • Online ISBN: 978-3-319-70090-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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