Abstract: The existing text recognition methods will cause decreased recognition accuracy due to not enough network depth.The paper addresses this issue and proposes an improved end-to-end text recognition network structure.Firstly, the algorithm takes the text as a sequence, and uses the residual module to divide the text into columns for the recurrent layer.This residual structureincreases network depth, to maintain the network’s best representation of the text image.It can capture the best feature representation of text images.Meanwhile, the residual module uses the stacked layer to learn the residual mapping to improve the convergence of the network though the number of layers is obviously increased.Secondly, we use the recurrent layer to model the context of these text features, and the modeling results will be taken into the softmax layer to predict corresponding labels, which achieve the recognition of arbitrary length of texts.The recurrent layer uses the Long Short-Term Memory to learn the dependencies between texts and solve the gradient vanishing problem in long sequence training.Finally, text label transcription and decoding are performed by the optimal path method.The method finds a path to maximize its probability, and outputs the sequence corresponding to the path as the optimal sequence.The improved text recognition network structure increases network depth, improves the feature description of text images and the stability under noises.In the experimental part, this paper compares with existing typical algorithms over the multiple test datasets (ICDAR2003, ICDAR2013, SVT and IIIT5K).The experiments show that the network structure can obtain better text recognition accuracy and verify the effectiveness of the proposed network structure.

Key words: Network depth, Optimal path, Residual connection, Scene text recognition, Stacked layer