Skip to main content
SpringerLink
Log in

Handwritten Chinese text editing and recognition system

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper describes a handwritten Chinese text editing and recognition system that can edit handwritten text and recognize it with a client-server mode. First, the client end samples and redisplays the handwritten text by using digital ink technics, segments handwritten characters, edits them and saves original handwritten information into a self-defined document. The self-defined document saves coordinates of all sampled points of handwriting characters. Second, the server recognizes handwritten document based on the proposed Gabor feature extraction and affinity propagation clustering (GFAP) method, and returns the recognition results to client end. Moreover, the server can also collect the labeled handwritten characters and fine tune the recognizer automatically. Experimental results on HIT-OR3C database show that our handwriting recognition method improves the recognition performance remarkably.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Avola D, Caschera MC, Ferri F, Grifoni P (2010) Classifying and resolving ambiguities in sketch based interaction. Int J Virt Tech Multimed 1(2):104–139

    Article  Google Scholar 

  2. Ding K, Jin L, Liu G, Gao J, Mei Z (2011) Incremental learning of mqdf for writer adaptation. ICIC-EL, Part B: Applications 2:25–30

    Google Scholar 

  3. Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972–976

    Article  MATH  MathSciNet  Google Scholar 

  4. Holzinger A, Schloegl M, Peischl B, Debevc M (2010) Preferences of handwriting recognition on mobile information systems in medicine: improving handwriting algorithm on the basis of real-life usability research. In: International joint conference on e-business and telecommunications, 26–28 Jul 2010. IEEE, Athens, pp 120–123

    Google Scholar 

  5. Holzinger A, Basic L, Peischl B, Debevc M (2011) Handwriting recognition on mobile devices: state of the art technology, usability and business analysis. In: International conference on electronic business and telecommunications, 18–21 Jul 2011. Springer, Seville, Spain, pp 219–227

    Google Scholar 

  6. Holzinger A, Schloegl M, Peischl B, Debevc M (2012) Optimization of a handwriting recognition algorithm for a mobile enterprise health information system on the basis of real-life usability research. CCIS 222:97–111

    Google Scholar 

  7. Huang Z, Ding K, Jin L, Gao X (2009) Writer adaptive online handwriting recognition using incremental linear discriminant analysis. In: International Conference on Document Analysis and Recognition, 26–29 Jul 2009. IEEE, Barcelona, Spain, pp 91–95

    Chapter  Google Scholar 

  8. Jin L, Ding K, Huang Z (2010) Incremental learning of lda model for chinese writer adaptation. Neurocomputing 73(10–12):1614–1623

    Article  Google Scholar 

  9. Jin LW, Gao Y, Liu G, Li YY, Ding K (2011) Scut-couch2009-a comprehensive online unconstrained chinese handwriting database and benchmark evaluation. Int J Doc Anal Recognit 14(1):53–64

    Article  Google Scholar 

  10. Kim IJ, Kim JH (2003) Statistical character structure modeling and its application to handwritten chinese character recognition. IEEE Trans Pattern Anal Mach Intell 25(11):1422–1436

    Article  Google Scholar 

  11. Kimura F, Takashina K, Tsuruoka S, Miyake Y (1987) Modified quadratic discriminant functions and the application to chinese character-recognition. IEEE Trans Pattern Anal Mach Intell 9(1):149–153

    Article  Google Scholar 

  12. Liu CL, Marukawa K (2005) Pseudo two-dimensional shape normalization methods for handwritten chinese character recognition. Pattern Recogn 38(12):2242–2255

    Article  Google Scholar 

  13. Liu CL, Zhou XD (2006) Online japanese character recognition using trajectory-based normalization and direction feature extraction. In: International Workshop on Frontiers in Handwriting Recognition, 23–26 Oct 2006. Suvisoft, La Baule, France, pp 217–222

    Google Scholar 

  14. Liu CL, Jaeger S, Nakagawa M (2004) Online recognition of chinese characters: the state-of-the-art. IEEE Trans Pattern Anal Mach Intell 26(2):198–213

    Article  Google Scholar 

  15. Liu CL, Nakashima K, Sako H, Fujisawa H (2004) Handwritten digit recognition: investigation of normalization and feature extraction techniques. Pattern Recogn 37(2):265–279

    Article  MATH  Google Scholar 

  16. Long T, Jin L (2008) A novel orientation free method for online unconstrained cursive handwritten chinese word recognition. In: International Conference on Pattern Recognition, 4–6 Dec 2008. IEEE, Orlando, Florida, USA, pp 1–4

    Chapter  Google Scholar 

  17. Long T, Jin LW (2008) Building compact mqdf classifier for large character set recognition by subspace distribution sharing. Pattern Recogn 41(9):2916–2925

    Article  MATH  Google Scholar 

  18. Plamondon R, Srihari SN (2000) Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 22(1):63–84

    Article  Google Scholar 

  19. Wang DH, Liu CL, Yu J, Zhou XD (2009) Casia-olhwdb1: a database of online handwritten chinese characters. In: International conference on document analysis and recognition, 26–29 Jul 2009. IEEE, Barcelona, Spain, pp 1206–1210

    Chapter  Google Scholar 

  20. Wang QF, Yin F, Liu CL (2009) Integrating language model in handwritten chinese text recognition. In: International conference on document analysis and recognition, 26–29 Jul 2009. IEEE, Barcelona, Spain, pp 1036–1040

    Chapter  Google Scholar 

  21. Wang XW, Ding XQ, Liu CS (2005) Gabor filters-based feature extraction for character recognition. Pattern Recogn 38(3):369–379

    Article  MATH  MathSciNet  Google Scholar 

  22. Welling M (2006) Fisher linear discriminant analysis. Tech. rep., Department of Computer Science, University of Toronto

  23. Wu T, Ma S (2003) Feature extraction by hierarchical overlapped elastic meshing for handwritten chinese character recognition. In: International conference on document analysis and recognition, 3–6 Aug 2003. IEEE, Edinburgh, Scotland, UK, pp 529–533

    Google Scholar 

  24. Zhang HP, Yu HK, Xiong DY, Liu Q (2003) Hhmm-based chinese lexical analyzer ictclas. In: SIGHAN workshop on Chinese language processing, 11–12 Jul 2003. ACM, Sapporo, Japan, pp 184–187

    Google Scholar 

  25. Zhang P, Bui TD, Suen CY (2007) A novel cascade ensemble classifier system with a high recognition performance on handwritten digits. Pattern Recogn 40(12):3415–3429

    Article  MATH  Google Scholar 

  26. Zhao SY, Chi ZR, Shi PF, Yan H (2003) Two-stage segmentation of unconstrained handwritten chinese characters. Pattern Recogn 36(1):145–156

    Article  MATH  Google Scholar 

  27. Zhou S, Chen Q, Wang X (2010) Hit-or3c: An opening recognition corpus for chinese characters. In: International workshop on document analysis systems, 9–11 June 2010. ACM, Boston, MA, USA, pp 223–230

    Google Scholar 

Download references

Acknowledgements

We would like to thank Xinggang Fu, Huili Li, Xinyi Guo, Hui Li, Suqin Ao, Zou Chen, and Junqi Pu for their contributions in handwritten Chinese text editing and recognition system. We would also like to thank all the volunteers for their contributions in HIT-OR3C. We would also like to thank Chenglin Liu et al. in Chinese Academy of Sciences, and Lianwen Jin et al. in South China University of Technology, for their providing of CASIA-OLHWDB1 and SCUT-COUCH2009 databases. This work was supported in part by the National Natural Science Foundation of China (No. 61173075 and No. 60973076), and Shenzhen Foundation Research Plan (JC201005260175A).

Author information

Authors and Affiliations

  1. Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, People’s Republic of China

    Shusen Zhou, Qingcai Chen & Xiaolong Wang

Authors
  1. Shusen Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qingcai Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaolong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingcai Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhou, S., Chen, Q. & Wang, X. Handwritten Chinese text editing and recognition system. Multimed Tools Appl 71, 1363–1380 (2014). https://doi.org/10.1007/s11042-012-1270-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-012-1270-2

Keywords

Search

Navigation