A new perspective: Recognizing online handwritten Chinese characters via 1-dimensional CNN

Abstract

For online handwritten Chinese character recognition (OLHCCR), it has become a popular choice to employ the 2-dimensional convolutional neural network (2-D CNN) in recognizing the extracted feature images or utilize the recurrent neural network (RNN) to classify handwriting trajectories directly. Instead, here we propose to apply the 1-dimensional CNN (1-D CNN), which, to our knowledge, is novel in the context of OLHCCR. Specifically, a 1-D CNN is engaged upon the sequential handwriting trajectories. Each output sequence is then averaged over time to form a fixed-size vector representation, upon which the final classification is made via a softmax operation. Compared with the 2-D CNN architecture, our approach is capable of delivering better results with a more compact model. This is achieved without adopting the computationally demanding techniques that are necessary when working with the 2-D CNN, including data augmentation and feature image extraction. Furthermore, our method attains a faster test time speed compared with the RNN, and this is more pronounced in processing long sequences. Empirically, our method yields the near-state-of-the-art accuracy of 98.11% on ICDAR 2013 competition dataset, and the state-of-the-art accuracy of 97.14% on in-air handwriting dataset IAHCC-UCAS2016, respectively.