Res-RNN Network and Its Application in Case Text Recognition

To solve the problem of poor feature extraction ability of traditional text recognition methods in Chinese medical record text, this paper proposes a Res-RNN network for feature extraction based on residual error. Combined with residual characteristics, this network not only improves the depth of the network, but also ensures that there will be no degradation of the network, and strengthens the network's ability to extract Chinese character features. In the residual module, 1 x 1 convolution kernel is used to replace 3 x 3 convolution kernel, effectively reducing the parameters. Combined with feature maps of different scales, the feature information of Chinese characters at different levels is effectively utilized. According to the characteristics of Chinese characters, the vertical sensing field of the feature map is adjusted to retain more vertical fine-grained feature information, thus effectively improving the representational ability of the network. Experiments on actual Chinese medical record text image data set show that the accuracy of the proposed model is 4% higher than that of CRNN.