A Unified CNN-RNN Approach for in-Air Handwritten English Word Recognition
Ji Gan, Weiqiang Wang, Ke Lü · 2018
As a new human-computer interaction application, in-air handwriting allows the user to write in the air in a natural way. In this paper, we propose a unified CNN-RNN approach for in-air handwritten English word recognition (IAHEWR), which integrates the advantages of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Specifically, the proposed approach follows an encoder-decoder framework, where the encoder is a deep CNN for efficiently processing the input temporal-sequential features, and the decoder is a RNN for accurately generating the target character sequence. We evaluate the proposed approach on an in-air handwritten English word dataset IAHEW-UCAS2016, and the experimental results demonstrate that the proposed approach achieves the comparable recognition accuracy and much higher computation efficiency when compared with the state-of-the-art approach for IAHEWR.