A new hybrid-parameter recurrent neural network for online handwritten chinese character recognition

Abstract

Bidirectional RNN (BRNN) is a common method of modeling time series and has been widely applied in many areas, including speech recognition, machine translation, natural language processing, scene text recognition. Compared with the unidirectional RNN, the bidirectional RNN usually obtains better performance but higher computational cost due to additional backward processing of an input sequence. In this paper, we present a new hybrid-parameter RNN which consists of two virtual unidirectional recurrent neural networks. The computational cost of the proposed RNN is only three-fourths of that of the bidirectional RNNs. In addition, we accumulate the feature vectors from different layers to obtain the output of the RNN system, which is an efficient way to combine all the feature vectors without increasing the model size. The experimental results on IAHCC-UCAS2016 dataset and ICDAR2013 competition database show that the hybrid-parameter RNN obtains a better recognition performance with lower computational cost, compared with the bidirectional RNN.