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Liu, W.J., Liang, X.J. and Qu, H.C. (2016) Learning Performance of Convolutional Neural Networks with Different Pooling Models. Journal of Image and Graphics, 21, 1178-1190.

has been cited by the following article:

• TITLE: Optimization of Convolutional Neural Network for Recognition of Vehicle Frame Number

  AUTHORS: Haiming Li, Yongxue Liu, Yong Wang

  KEYWORDS: Frame Number Recognition, Convolutional Neural Network (CNN), Feature Extraction, Pooling

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.11, November 27, 2018

  ABSTRACT: With the development of the economy and the surge in car ownership, the sale of used cars has been welcomed by more and more people, and the information of the vehicle condition is the focus information of them. The frame number is a unique number used in the vehicle, and by identifying it can quickly find out the vehicle models and manufacturers. The traditional character recognition method has the problem of complex feature extraction, and the convolutional neural network has unique advantages in processing two-dimensional images. This paper analyzed the key techniques of convolutional neural networks compared with traditional neural networks, and proposed improved methods for key technologies, thus increasing the recognition of characters and applying them to the recognition of frame number characters.