Engineering Character Recognition Algorithm and Application Based on BP Neural Network

  • Chen RongEmail author
  • Yu Luqian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)


Character recognition algorithm can directly affect the accuracy and speed of character recognition. This algorithm uses BP neural network to train samples, preserve neural network weights, and recognize photographed images. The software algorithm integrates image-processing and neural network modules. Image-processing modules include pre-treatment processes, such as, binaryzation, denoising, dilation, erosion, rotation and character segmentation and extraction of images collected by cameras. Neural network modules include network training, identification, display, saving, loading, and other modules, such as image preprocessing and recognition. A prototype of online engineering character recognition system has been developed. Test results indicate that the duration of a single picture is approximately 100 ms, and the detection time displayed by the interface includes the zooming time of display interface that is approximately 200 ms.


Engineering character recognition BP neural network Online automatic recognition 


  1. 1.
    Kobchaisawat, T., Chalidabhongse, T.H.: Thai text localization in natural scene images using convolutional neural network. In: 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp. 1–7. IEEE (2014)Google Scholar
  2. 2.
    Guo, Q., Lei, J., Tu, D., Li, G.: Reading numbers in natural scene images with convolutional neural networks. In: 2014 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), pp. 48–53. IEEE (2014)Google Scholar
  3. 3.
    Xu, H., Su, F.: A robust hierarchical detection method for scene text based on convolutional neural networks. In: IEEE International Conference on Multimedia & Expo, pp. 1–6 (2015)Google Scholar
  4. 4.
    Wang, G.: Detecting text in natural scene images with conditional clustering and convolution neural network. J. Electron. Imaging 24(5), 053019 (2015)CrossRefGoogle Scholar
  5. 5.
    Yang, J.: Practical Course on Artificial Neural Networks. Publishing House of Zhejiang University, Hangzhou (2001)Google Scholar
  6. 6.
    Sarfraz, M., Ahmed, M.J., Ghazi, S.A.: Saudi Arabian license plate recognition system. In: 2003 Proceedings International Conference on Geometric Modeling and Graphics, pp. 36–41. IEEE (2003)Google Scholar
  7. 7.
    Kunyan, Z., Yiya, Z., Songchi, M., Guijuan, W.: A BP neural network license plate character recognition system based on global threshold two valued method. Comput. Eng. Sci. (02), 88–90+134 (2010)Google Scholar
  8. 8.
    Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: Binary robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555. IEEE (2011)Google Scholar
  9. 9.
    Nijhuis, J.A.G., Ter Brugge, M.H., Helmholt, K.A., Pluim, J.P.W., Spaanenburg, L., Venema, R.S., Westenberg, M.A.: Car license plate recognition with neural networks and fuzzy logic. In: 1995 Proceedings of IEEE International Conference on Neural Networks, vol. 5, pp. 2232–2236. IEEE (1995)Google Scholar
  10. 10.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Audit S&T Research InstitutionNanjing Audit UniversityNanjingChina
  2. 2.School of Government AuditNanjing Audit UniversityNanjingChina

Personalised recommendations