关键词: 图像识别, 深度学习, 余弦相关性, 卷积神经网络, 手写汉字

Abstract:

In order to improve the recognition rate and enhance feature extraction capability of Convolutional Neural Network（CNN）, a method for CNN with cosine similarity algorithm is proposed. It has better recognition results under harsh conditions such as blurring and uneven illumination. So a cosine similarity algorithm is added to the convolutional layer of the CNN. The method has the advantages of stronger pattern detection ability, faster convergence speed and higher accuracy than traditional CNN. By adding a cosine similarity algorithm to the convolutional layer of the CNN, the handwritten Chinese characters recognition experiments based on the traditional CNN method and cosine similarity CNN method respectively are completed, with the same training parameters and the same number of layers. After 20 experiments, the results show that the recognition rate of cosine similarity CNN is 2.01% higher and convergence speed is faster than the traditional CNN. Finally, the accuracy, loos function and time complexity are compared with the current popular algorithms on the MNIST dataset. It is concluded that the Convolutional Neural Network（CNN） combined with cosine similarity algorithm has certain advantages in accuracy and loss function. But further improvements are needed in terms of time complexity.

Key words: image recognition, deep learning, cosine similarity algorithm, Convolution Neural Network（CNN）, Chinese characters