Authors: Lulu Xu
Description: For text line recognition, we implement an optimized convolutional recurrent neural network which was first proposed by Baoguang Shi. The proposed network can handle sequences in arbitrary lengths. In CRNN model, the feature extraction part is fully convolution network (FCN) based on a simplified Inception-Renset network which was build by 20 convolution layers and 3 max-pool layers. The FCN network is followed by recurrent neural network which was build by 4 LSTMs, two forward and two backward. RNN predicts each column of the last feature map in FCN, the predicted distributions is fed into Connectionist Temporal Classification (CTC) layer.