Handwritten Numeral Recognition Based on Improved LeNet-5 Model
Deng Changyin;Zhang Jie;college of communication engineering, Chengdu University of Information Technology;
Based on the convolution neural network, the LeNet-5 model is improved, and the neural network model which is more suitable for handwritten numeral recognition is established. The improved model and the network training recognition process are introduced in detail. The improved model is validated by MNIST character database, and the influence of parameters such as the number of different volume maps and the number of training per batch on the final recognition performance is analyzed and compared with several commonly used identification methods. Through the results can be seen, the improved new network structure is simple, high recognition, recognition speed, with good robustness, generalization ability and so on. It shows that the improved neural network model has a good recognition performance for handwritten numerals, which can meet the practical application requirements.
【CateGory Index】： TP183;TP391.41