method: LSTM based text recognition2017-06-28

Authors: Lulu Xu

Description: For text line recognition, we implement an optimized convolutional recurrent neural network which was first proposed by Baoguang Shi[1]. 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.