Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (9): 207-211.DOI: 10.3778/j.issn.1002-8331.2002-0015

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Improved Handwritten Date Recognition in Scanned Documents Combined with LeNet-5

ZHANG Cheng, DAI Junfeng, XIONG Wenxin   

  1. 1.State Grid Hubei Information & Telecommunication Company Limited, Wuhan 430077, China
    2.School of Electronic Information, Wuhan University, Wuhan 430072, China
  • Online:2021-05-01 Published:2021-04-29



  1. 1.国网湖北省电力有限公司 信息通信公司,武汉 430077
    2.武汉大学 电子信息学院,武汉 430072


This paper studies the application of LeNet-5 in handwritten date characters recognition in scanned documents. As various noises will appear in the process of document scanning, especially light and color interference, using LeNet-5 algorithm directly can not get good results. This article firstly gets the location and division of the particular character to be recognized in the whole document while the divided character image is processed by denoising, graying and binarization. For the next step, character image is segmented into a single character and then the article realizes the recognition of handwritten date characters on the basis of LeNet-5 network combined with model matching method. By comparing the recognition effect under different combination of parameters and adjusting the parameters to improve model performance for practical objects, an algorithm that can achieve a better recognition effect for handwritten date character set is realized. Experimental results show that the algorithm is effective and can be applied in practical engineering.

Key words: deep learning, convolutional neural network, LeNet-5, character recognition



关键词: 深度学习, 卷积神经网络, LeNet-5, 字符识别