A SequencetoSequence Transformer Premised Temporal Convolutional Network for Chinese Word Segmentation The prevalent approaches for the task of Chinese word segmentation almost rely on the BiLSTM neural network. However, the methods based the BiLSTM have an inherent drawback: the Vanishing Gradients, which cause the little efficient in capturing the faraway character information of a long sentence for the task of word segmentation. In this work, we propose a novel sequencetosequence transformer model for Chinese word segmentation, which is premised a type of convolutional neural network named temporal convolutional network. The model uses the temporal convolutional network to construct an encoder, and uses a fullyconnected neural network to build a decoder, and applies the Viterbi algorithm to build an inference layer to infer the final result of the Chinese word segmentation. Meanwhile, the model captures the faraway character information of a long sentence by adding the layers of the encoder. For achieving a superior result of word segmentation, the model binds the Conditional Random Fields model to train parameters. The experiments on the Chinese corpus show that the performance of Chinese word segmentation of the model is better than the BiLSTM model, and the model has a better ability to process a long sentence than the BiLSTM.

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.