A New Hybrid-parameter Recurrent Neural Networks for Online Handwritten Chinese Character Recognition

TL;DR

This paper introduces a novel deep RNN with hybrid parameters and a new Memory Pool Unit for online handwritten Chinese character recognition, achieving improved accuracy and efficiency over existing models.

Contribution

It proposes a hybrid-parameter RNN architecture and a new Memory Pool Unit, enhancing temporal learning and model capacity for Chinese character recognition.

Findings

Higher recognition accuracy on benchmark datasets

Fewer parameters and faster recognition speed

Memory Pool Unit achieves competitive results

Abstract

The recurrent neural network (RNN) is appropriate for dealing with temporal sequences. In this paper, we present a deep RNN with new features and apply it for online handwritten Chinese character recognition. Compared with the existing RNN models, three innovations are involved in the proposed system. First, a new hidden layer function for RNN is proposed for learning temporal information better. we call it Memory Pool Unit (MPU). The proposed MPU has a simple architecture. Second, a new RNN architecture with hybrid parameter is presented, in order to increasing the expression capacity of RNN. The proposed hybrid-parameter RNN has parameter changes when calculating the iteration at temporal dimension. Third, we make a adaptation that all the outputs of each layer are stacked as the output of network. Stacked hidden layer states combine all the hidden layer states for increasing the expression capacity. Experiments are carried out on the IAHCC-UCAS2016 dataset and the CASIA-OLHWDB1.1 dataset. The experimental results show that the hybrid-parameter RNN obtain a better recognition performance with higher efficiency (fewer parameters and faster speed). And the proposed Memory Pool Unit is proved to be a simple hidden layer function and obtains a competitive recognition results.