关键词: 深度学习, 卷积神经网络, 票据识别, 票据校正, 字符识别, 文本定位

Abstract:

This bill recognition method based on combined network has been constructed to satisfy the tremendous needs of bill entering and solve the problems of low efficiency and low accuracy of data inputting with traditional man-powered method in medical industry. The parameters of the traditional target detection network describe the location and category of the target at the same time, it is hard to train the network because of the large number of parameters, a recognition method combining Faster RCNN and deep convolutional neural network is proposed to solve those problems, and the combined network locates and identifies targets step by step to lower the complexity of the task. The self-built bill training set and character-based training set are used to do network training, the validity of the algorithm is verified by the bill collected on site, the optimal parameters are selected by testing the performance of the model under different parameters, and the performance of this method is analyzed with the traditional method. It shows that the recognition accuracy reaches 95.4%, the recall rate reaches 95.4%, and the speed reaches 0.76 secends per piece.

Key words: Deep Learning, Convolutional Neural Network（CNN）, bill recognition, bill correction, character recognition, text location