Multi-type Digital Recognition Based on TensorFlow

摘要

For the problem of inaccurate identification of numbers, this paper is based on LeNet-5 network structure and optimizes it. The objective function and optimizer are added after the network output, and the sample library is updated to make it more accurate to identify multiple types of numbers. The optimized architecture is applied to identify multiple types of numbers, trained and tested, and the optimization parameters are selected by comparison. The experimental results show that the optimization parameters of certain values have a higher recognition rate for identifying numbers. The study has reference value for multi-type digital recognition.