基于深度学习的手写文字识别

Handwritten Word Recognition Based on Deep Learning

当前越来越多的场景需要将手写体的文字转换为电子格式,手写体识别成为人机交互最便捷的手段之一,拥有广泛的应用前景。文章提出了一种基于TensorFlow框架的深度学习手写识别方法,包含手写数字识别和手写汉字识别。以TensorFlow为框架,采用CNN神经网络模型建立训练集以降低识别错误率。实验结果最终表明,对手写数字的识别率达到95%,对手写汉字的识别率达到90%。

At present,more and more scenes need to convert handwritten words into electronic format.Handwritten recognition has become one of the most convenient means of human-computer interaction and has a wide application prospect.This paper proposes a deep learning handwriting recognition method based on TensorFlow framework,including handwritten numeral recognition and handwritten Chinese character recognition.Taking TensorFlow as the framework,the CNN neural network model is used to establish a training set to reduce the recognition error rate.The experimental results finally show that the recognition rate of handwritten numeral reaches 95%,and the recognition rate of handwritten Chinese characters reaches 90%.