In-Air Handwritten Chinese Text Recognition with Attention Convolutional Recurrent Network

Abstract

In-air handwriting is a new and more humanized way of human-computer interaction, which has a broad application prospect. One of the existing online handwritten Chinese text recognition model is to convert the trajectory data into image-like representation and use two-dimensional convolutional neural network (2DCNN) for feature extraction, and the another one directly process trajectory sequence with Long Short-Term Memory (LSTM). However, when using 2DCNN, many information will be lost in the process of conversion into images. When using LSTM, LSTM network is easy to cause gradient problem. So we propose an attention convolutional recurrent network (ACRN) for in-air handwritten Chinese text, which introduces one-dimensional convolutional neural network (1DCNN) containing dilation convolution for feature extraction of trajectory data directly. After that, the ACRN uses LSTM combined with multihead attention mechanism to focus on some key words in handwritten Chinese text, mines multi-level dependencies and outputs to softmax for classification. Finally the ACRN uses the Connectionist Temporal Classification (CTC) objective function without input-output alignment to decode the coding results. We conduct experiments on the CASIA-OLHWDB2.0-2.2 dataset and in-air handwritten Chinese text dataset IAHCT-UCAS2018. Experimental results demonstrate that compared with previous methods, our method obtains a more compact model with a higher recognition accuracy.