Chinese Character Images Recognition and Its Application in Mobile Platform

Gang Gu, Jiangqin Wu, Tianjiao Mao, Pengcheng Gao

2016

Abstract

Chinese characters are profound and polysemantic. Reading a Chinese character is a procedure of image understanding, if the Chinese character is captured as an image. Due to the complexity of structure and plenty of Chinese characters, there always exist some unfamiliar characters when reading books, so it would be great if a tool is provided to help users understand the meaning of unknown characters. We propose a method that combines global and local features(i.e., GIST and SIFT features) to recognize the Chinese character images captured from mobile camera. Three schemes are investigated based on practical considerations. Firstly,the so-called GIST and SIFT descriptors extracted from Chinese character images are adopted purely as features. Then filter the SIFT feature points of similar Chinese character images based on GIST feature. Finally, compress the storage of GIST and SIFT descriptors to accommodate mobile platform with Similarity Sensitive Coding(SSC) algorithm. At the stage of recognition, the top 2k Chinese characters are firstly obtained by hamming distance in GIST feature space, then reorder the selected characters as final result by SIFT feature. We build an Android app that implements the recognition algorithm. Experiment shows satisfying recognition results of our proposed application compared to other Android apps.

References

  1. Barbu, T. (2009). Content-based image retrieval using gabor filtering. In Database and Expert Systems Application, 2009. DEXA'09. 20th International Workshop on, pages 236-240. IEEE.
  2. Calonder M, Lepetit V, S. C. (2010). Brief: Binary robust independent elementary features. In Computer VisionCECCV 2010. Springer Berlin Heidelberg, pages 778-792. Springer.
  3. Jin, Z., Qi, K., Zhou, Y., Chen, K., Chen, J., and Guan, H. (2009). Ssift: An improved sift descriptor for chinese character recognition in complex images. In Computer Network and Multimedia Technology, 2009. CNMT 2009. International Symposium on, pages 1-5. IEEE.
  4. Lin, Y., Wu, J., Gao, P., Xia, Y., and Mao, T. (2013). Lshbased large scale chinese calligraphic character recognition. In Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries, pages 323-330. ACM.
  5. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  6. Mikolajczyk K, S. C. (2005). A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(10):1615-1630.
  7. Ni, K., Kannan, A., Criminisi, A., and Winn, J. (2008). Epitomic location recognition. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8. IEEE.
  8. Oliva, A. and Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3):145-175.
  9. Pengcheng, G., Jiangqin, W., Yuan, L., Yang, X., and Tianjiao, M. (2014). Fast chinese calligraphic character recognition with large-scale data. Multimedia Tools and Applications, pages 1-18.
  10. Rosenberg, A. and Dershowitz, N. (2012). Using SIFT Descriptors for OCR of Printed Arabic. PhD thesis, Citeseer.
  11. Shakhnarovich G, Viola P, D. T. (2003). Fast pose estimation with parameter-sensitive hashing. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, pages 750-757. IEEE.
  12. Tianjiao, M., Jiangqin, W., Pengcheng, G., Yang, X., and Yuan, L. (2013). Calligraphy word style recognition by knn based feature library filtering. In 3rd International Conference on Multimedia Technology (ICMT13). Atlantis Press.
  13. Zhang Z, Jin L, D. K. (2009). Character-sift: a novel feature for offline handwritten chinese character recognition. In Document Analysis and Recognition, 2009. ICDAR'09. 10th International Conference on, pages 763-767. IEEE.
  14. Zhen-Yan, W. (2014). Chinese character recognition method based on image processing and hidden markov model. In Intelligent Systems Design and Engineering Applications (ISDEA), 2014 Fifth International Conference on, pages 276-279. IEEE.
  15. Zhuang, Y., Zhang, X., Lu, W., and Wu, F. (2005). Webbased chinese calligraphy retrieval and learning system. In Advances in Web-Based Learning-ICWL 2005, pages 186-196. Springer.
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Paper Citation


in Harvard Style

Gu G., Wu J., Mao T. and Gao P. (2016). Chinese Character Images Recognition and Its Application in Mobile Platform . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 311-318. DOI: 10.5220/0005679703110318


in Bibtex Style

@conference{visapp16,
author={Gang Gu and Jiangqin Wu and Tianjiao Mao and Pengcheng Gao},
title={Chinese Character Images Recognition and Its Application in Mobile Platform},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={311-318},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005679703110318},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Chinese Character Images Recognition and Its Application in Mobile Platform
SN - 978-989-758-175-5
AU - Gu G.
AU - Wu J.
AU - Mao T.
AU - Gao P.
PY - 2016
SP - 311
EP - 318
DO - 10.5220/0005679703110318