Fast Image Embedded Chinese Text Extracting by Homogeneous Space Mapping

Li Ying; Liu Lisha; Cui Yan-peng; Zhuang Huaiyu

doi:doi:10.11648/j.jeee.20170503.11

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Fast Image Embedded Chinese Text Extracting by Homogeneous Space Mapping

Li Ying, Liu Lisha, Cui Yan-peng, Zhuang Huaiyu

Published in Journal of Electrical and Electronic Engineering (Volume 5, Issue 3)

Received: 14 May 2017 Published: 16 May 2017

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Abstract

Text-embedded images are popular in the mobile Internet to spread malicious information. A fast text-embedded image Chinese text extracting algorithm based on homogeneous space mapping is proposed. Image enhancement functions are used to highlight edge and texture features of images. Sobel operator is used to extract the edge feature and wavelet packet is used to extract the 24-dimensional texture feature vectors in the enhanced images. The texture features and edge features are used to describe the homogeneity of an image, which construct the homogeneous feature map of the image. The differences between the non-text and the text region homogeneity are used to distinguish them and reduce non-text region further. Thus the text regions are highlighted. Then, homogeneous text samples are used to train the text region detector, which greatly reduces the computational complexity. Finally, the characters are segmented and recognized. Some experiments to verify the validity and practicability of the proposed algorithm have been conducted. The recognition rate achieves 86%, which is higher than that of other methods in industry. The algorithm is verified on the operator's malicious information monitoring system, which provides secure malicious filtering performance.

Published in	Journal of Electrical and Electronic Engineering (Volume 5, Issue 3)
DOI	10.11648/j.jeee.20170503.11
Page(s)	86-91
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Chinese Text Embedded Image, Homogeneous Mapping, Text Extraction, Information Security

References

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[4]	Zhao Ming Li Shutao, Kwok James. Text detetion in images using sparse representation with discriminative dictionaries [J]. Image and Vision Computing, 2010, 28(12):1590-1599.
[5]	Park Jonghyun, Lee Gueesang, Kim Euichul,et al. Automatic detection and recognition of Korean text in outdoor signboard image [J], Pattern Recognition Letters. 2010, 31(12):1728-1239.
[6]	Ikica, Andrej, Peer, Peter. An improved edge profile based method for text detection in images of natural scenes [C] EUROCON-Intenational Conference on Computer as a Tool (EUROCON). IEEE, 2011:1-4.
[7]	Yan Jianqiang, Tao Dacheng, Tian Cunna, Gao Xinbo, Li Xulong. Chinese Text Detection and Location for Images in Multimedia Messaging Service[C], //Proc of Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on, Istanbul Turkey: IEEE Conference Publications,2010: 3896-3901.
[8]	Zhong Yu, Zhang Hongjiang, Anil K. Jain. Automatic Caption Localization in Compressed Video [J], IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(4):385-392.
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[14]	Huang Jianhua, Cheng Hengda, Wu Rui. Text Detection Method Based on Fuzzy Homogeneity Mapping [J]. Journal of Electronics and Information Technology, 2008, 30(6): 1376-1380.

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APA Style

Li Ying, Liu Lisha, Cui Yan-peng, Zhuang Huaiyu. (2017). Fast Image Embedded Chinese Text Extracting by Homogeneous Space Mapping. Journal of Electrical and Electronic Engineering, 5(3), 86-91. https://doi.org/10.11648/j.jeee.20170503.11

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ACS Style

Li Ying; Liu Lisha; Cui Yan-peng; Zhuang Huaiyu. Fast Image Embedded Chinese Text Extracting by Homogeneous Space Mapping. J. Electr. Electron. Eng. 2017, 5(3), 86-91. doi: 10.11648/j.jeee.20170503.11

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AMA Style

Li Ying, Liu Lisha, Cui Yan-peng, Zhuang Huaiyu. Fast Image Embedded Chinese Text Extracting by Homogeneous Space Mapping. J Electr Electron Eng. 2017;5(3):86-91. doi: 10.11648/j.jeee.20170503.11

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@article{10.11648/j.jeee.20170503.11,
  author = {Li Ying and Liu Lisha and Cui Yan-peng and Zhuang Huaiyu},
  title = {Fast Image Embedded Chinese Text Extracting by Homogeneous Space Mapping},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {5},
  number = {3},
  pages = {86-91},
  doi = {10.11648/j.jeee.20170503.11},
  url = {https://doi.org/10.11648/j.jeee.20170503.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20170503.11},
  abstract = {Text-embedded images are popular in the mobile Internet to spread malicious information. A fast text-embedded image Chinese text extracting algorithm based on homogeneous space mapping is proposed. Image enhancement functions are used to highlight edge and texture features of images. Sobel operator is used to extract the edge feature and wavelet packet is used to extract the 24-dimensional texture feature vectors in the enhanced images. The texture features and edge features are used to describe the homogeneity of an image, which construct the homogeneous feature map of the image. The differences between the non-text and the text region homogeneity are used to distinguish them and reduce non-text region further. Thus the text regions are highlighted. Then, homogeneous text samples are used to train the text region detector, which greatly reduces the computational complexity. Finally, the characters are segmented and recognized. Some experiments to verify the validity and practicability of the proposed algorithm have been conducted. The recognition rate achieves 86%, which is higher than that of other methods in industry. The algorithm is verified on the operator's malicious information monitoring system, which provides secure malicious filtering performance.},
 year = {2017}
}

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TY - JOUR
T1 - Fast Image Embedded Chinese Text Extracting by Homogeneous Space Mapping
AU - Li Ying
AU - Liu Lisha
AU - Cui Yan-peng
AU - Zhuang Huaiyu
Y1 - 2017/05/16
PY - 2017
N1 - https://doi.org/10.11648/j.jeee.20170503.11
DO - 10.11648/j.jeee.20170503.11
T2 - Journal of Electrical and Electronic Engineering
JF - Journal of Electrical and Electronic Engineering
JO - Journal of Electrical and Electronic Engineering
SP - 86
EP - 91
PB - Science Publishing Group
SN - 2329-1605
UR - https://doi.org/10.11648/j.jeee.20170503.11
AB - Text-embedded images are popular in the mobile Internet to spread malicious information. A fast text-embedded image Chinese text extracting algorithm based on homogeneous space mapping is proposed. Image enhancement functions are used to highlight edge and texture features of images. Sobel operator is used to extract the edge feature and wavelet packet is used to extract the 24-dimensional texture feature vectors in the enhanced images. The texture features and edge features are used to describe the homogeneity of an image, which construct the homogeneous feature map of the image. The differences between the non-text and the text region homogeneity are used to distinguish them and reduce non-text region further. Thus the text regions are highlighted. Then, homogeneous text samples are used to train the text region detector, which greatly reduces the computational complexity. Finally, the characters are segmented and recognized. Some experiments to verify the validity and practicability of the proposed algorithm have been conducted. The recognition rate achieves 86%, which is higher than that of other methods in industry. The algorithm is verified on the operator's malicious information monitoring system, which provides secure malicious filtering performance.
VL - 5
IS - 3
ER -

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Author Information

Li Ying

School of Electronic Engineering, Xidian Univercity, Xi'an, China
Liu Lisha

School of Electronic Engineering, Xidian Univercity, Xi'an, China
Cui Yan-peng

Institute for Internet Behavior, Xidian Univercity, Xi'an, China
Zhuang Huaiyu

China Mobile Group Guangdong Co.,Ltd. Guangzhou, China

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Plain Text BibTeX RIS

APA Style

Li Ying, Liu Lisha, Cui Yan-peng, Zhuang Huaiyu. (2017). Fast Image Embedded Chinese Text Extracting by Homogeneous Space Mapping. Journal of Electrical and Electronic Engineering, 5(3), 86-91. https://doi.org/10.11648/j.jeee.20170503.11

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ACS Style

Li Ying; Liu Lisha; Cui Yan-peng; Zhuang Huaiyu. Fast Image Embedded Chinese Text Extracting by Homogeneous Space Mapping. J. Electr. Electron. Eng. 2017, 5(3), 86-91. doi: 10.11648/j.jeee.20170503.11

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AMA Style

Li Ying, Liu Lisha, Cui Yan-peng, Zhuang Huaiyu. Fast Image Embedded Chinese Text Extracting by Homogeneous Space Mapping. J Electr Electron Eng. 2017;5(3):86-91. doi: 10.11648/j.jeee.20170503.11

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@article{10.11648/j.jeee.20170503.11,
  author = {Li Ying and Liu Lisha and Cui Yan-peng and Zhuang Huaiyu},
  title = {Fast Image Embedded Chinese Text Extracting by Homogeneous Space Mapping},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {5},
  number = {3},
  pages = {86-91},
  doi = {10.11648/j.jeee.20170503.11},
  url = {https://doi.org/10.11648/j.jeee.20170503.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20170503.11},
  abstract = {Text-embedded images are popular in the mobile Internet to spread malicious information. A fast text-embedded image Chinese text extracting algorithm based on homogeneous space mapping is proposed. Image enhancement functions are used to highlight edge and texture features of images. Sobel operator is used to extract the edge feature and wavelet packet is used to extract the 24-dimensional texture feature vectors in the enhanced images. The texture features and edge features are used to describe the homogeneity of an image, which construct the homogeneous feature map of the image. The differences between the non-text and the text region homogeneity are used to distinguish them and reduce non-text region further. Thus the text regions are highlighted. Then, homogeneous text samples are used to train the text region detector, which greatly reduces the computational complexity. Finally, the characters are segmented and recognized. Some experiments to verify the validity and practicability of the proposed algorithm have been conducted. The recognition rate achieves 86%, which is higher than that of other methods in industry. The algorithm is verified on the operator's malicious information monitoring system, which provides secure malicious filtering performance.},
 year = {2017}
}

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