An end-to-end recognizer for in-air handwritten Chinese characters based on a new recurrent neural networks

Haiqing Ren, Weiqiang Wang, Ke Lu, Jianshe Zhou, Qiuchen Yuan

Abstract: In-air handwriting is becoming a new human-computer interaction way. It is a challenging task to accurately recognizing in-air handwritten Chinese characters. In this paper, we present an end-to-end recognizer for in-air handwritten Chinese characters by using recurrent neural networks (RNN). Compared with the existing methods, the proposed RNN based methods does not need to explicitly extract features and directly take a sequence of dot locations as input. We have made two aspects of modifications on traditional RNN for improving the recognition accuracy. Concretely, the sum-pooling is performed on the states of each hidden layers, and a faster convergence in training can be obtained. Additionally, an assistant objective function is introduced into the conventional loss function, which brings a slight increase of performance. To evaluate the performance of the proposed method, the experiments are carried out on the IAHCC-UCAS2016 datasets to compare ours with other state-of-art methods. The experimental results show that the proposed RNN model has a fairly high recognition accuracy for in-air handwritten Chinese characters.