A Fast and Accurate Fully Convolutional Network for EndtoEnd Handwritten Chinese Text Segmentation and Recognition Handwritten Chinese Text Recognition (HCTR) is a challenging problem due to its high complexity. Previous methods based on oversegmentation, hidden Markov model (HMM) or long shortterm memory recurrent neural network (LSTMRNN) have achieved great success in recognition results. However, all of them, including oversegmentation based methods, are incompetent in accurate segmentation of single character. To solve this problem, we propose a fast and accurate fully convolutional network for endtoend segmentation and recognition of handwritten Chinese text. Experiments on CASIAHWDB datasets and ICDAR 2013 competition dataset show that our method achieves a competitive performance on recognition and produces great character segmentation results. Moreover, our model reaches a realtime speed of 70 fps, which is fast enough for various applications.

A Handwritten Chinese Text Recognizer Applying Multilevel Multimodal Fusion Network Handwritten Chinese text recognition (HCTR) has received extensive attention from the community of pattern recognition in the past decades. Most existing deep learning methods consist of two stages, i.e., training a text recognition network on the base of visual information, followed by incorporating language constrains with various language models. Therefore, the inherent linguistic semantic information is often neglected when designing the recognition network. To tackle this problem, in this work, we propose a novel multilevel multimodal fusion network and properly embed it into an attentionbased LSTM so that both the visual information and the linguistic semantic information can be fully leveraged when predicting sequential outputs from the feature vectors. Experimental results on the ICDAR2013 competition dataset demonstrate a comparable result with the stateoftheart approaches.