Abstract
In this paper, we design a recognition system of the handwritten numerals and English characters based on BP neural network. In the system, we first make some preprocess to the image. Secondly, we extract the structural and statistical features of the image. Thirdly, we train a model on the data sets via BP neural network. Finally, we can predict the test image using the trained model. The experiments show that the handwritten numbers recognition rate can reach more than 94 % and the handwritten English characters is 44 % respectively. Besides that, the recognition rate of Handwritten number and English together is 78 %. It has been proved that our method is effective and robust.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Patel, M.S., Reddy, S.L., Naik, A.J.: An efficient way of handwritten english word recognition. In: Satapathy, S.C., Biswal, B.N., Udgata, S.K., Mandal, J.K. (eds.) Proc. of the 3rd Int. Conf. on Front. of Intell. Comput. (FICTA) 2014. AISC, vol. 328, pp. 563–572. Springer, Heidelberg (2015)
Liu, C.-L., Yin, F., Wang, D.-H., Wang, Q.-F.: Online and offline handwritten Chinese character recognition: benchmarking on new databases. Pattern Recogn. 46, 155–162 (2013)
Wang, H., He, C.: Dynamical behavior of a cellular neural network. Acta Physica Sinica 52(10), 2409–2414 (2003)
Wei, Y., Xie, J., Wu, Q.: Research on license plate character recognition based on improved LM-BP. Neural Netw. 35(2), 48–57 (2016)
Deng, K.: An Automatic Marking System Based on Handwritten Character Recognition. Taiyuan University of Technology (2015)
Ma, C., Yu, M.: Analysis and extraction of structural features of off-line handwritten characters based on principal curves algorithm. Comput. Eng. Appl. 49(3), 202–206 (2013)
Zhao, X.: Research on Recognition Method of Handwritten Numbers and English Characters. Northeast Normal University (2010)
Fujisawa, H.: Forty years of research in character and document recognition–an industrial perspective. Pattern Recogn. 41(8), 2446–2453 (2008)
Acknowledgment
This work was supported by Natural Science Foundation of Fujian province of China (No. 2016J01325, No. 2015J05015), High-level Talents Foundation of Xiamen University of Technology (No. YKJ14014R and No. YKJ12025R), Science Planning Foundation of Xiamen City of China (No. 3502Z20130041 and No. 3502Z20133033), Scientific Research Fund Project of Talents of Hefei University (No. 15RC07), Natural Science Research Project of Universities of Anhui Province (No. KJ2015A206), Open Project Foundation of Intelligent Information Processing Key Laboratory of Shanxi Province (No. 2014001), Foundation of National Health and Family Planning Commission of the People’s Republic of China (No. WKJ-FJ-35), Foreign Cooperation and Communication of Science Foundation of Xiamen University of Technology (No. E201401000). The corresponding author was supported by CSC .
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, W. et al. (2017). Handwritten Numbers and English Characters Recognition System. In: Pan, JS., Snášel, V., Sung, TW., Wang, X. (eds) Intelligent Data Analysis and Applications. ECC 2016. Advances in Intelligent Systems and Computing, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-319-48499-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-48499-0_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48498-3
Online ISBN: 978-3-319-48499-0
eBook Packages: EngineeringEngineering (R0)