基于TensorFlow深度学习手写体数字识别及应用

Handwriting digital recognition and application based on TensorFlow deep learning

手写体数字的识别是人工智能识别系统中的重要组成部分。因个体手写数字的差异,现有识别系统准确率较低。基于TensorFlow深度学习框架完成手写体数字的识别及应用,首先建立TensorFlow深度学习框架,并分析了Softmax、卷积神经网络(CNN)模型结构,再对手写体数据集MNIST的60 000个样本进行深度学习,然后进行10 000个样本的测试对比,最后移植最优模型到Android平台进行应用。实测数据验证,相对于传统的Softmax模型,基于TensorFlow深度学习CNN模型识别率高达99.17%,提升了7.6%,为人工智能识别系统的发展提供了一定的科研价值。

The recognition of handwritten digits is an important part of the artificial intelligence recognition system.Due to the difference in individual handwritten numbers,the existing recognition system has a lower accuracy rate.This paper is based on the TensorFlow deep learning framework to complete the recognition and application of handwritten numbers.Firstly,the Softmax and Convolutional Neural Network(CNN)model structure is established and analyzed.Secondly,deep learning is performed on 60 000 samples of the handwritten data set MNIST,and then 10 000 samples are tested and compared.Finally,the optimal model is transplanted to the Android platform for application.Compared with the traditional Softmax model,the recognition rate based on TensorFlow deep learning CNN model is as high as 99.17%,an increase of 7.6%,which provides certain scientific research value for the development of artificial intelligence recognition system.