High-order directional features and sparse representation based classification for in-air handwritten Chinese character recognition

Abstract

The in-air handwriting is a natural and promising humancomputer interaction way. Compared with handwritten Chinese characters on touch screen, the in-air handwritten Chinese characters have their unique characteristics, e.g., each character is always written in a single stroke. In this paper, we propose a high-order directional feature for recognizing in-air handwritten Chinese characters. The proposed highorder features characterize the rate of direction change and the change rate of the rate of direction change, and can be easily be combined with the 8-directional features to get an enhanced version. Additionally, we exploit the locality-sensitive dictionary learning and sparse representation based classifier (LSRC) to recognize in-air handwritten Chinese characters. Since few work has applied the SRC in on-line handwritten Chinese character recognition (OHCCR), we evaluate the proposed system on both an in-air handwritten Chinese character dataset, the IAHCC-UCAS2015 dataset, and a handwritten Chinese character dataset, the SCUT-COUCH2009 database. The experimental results show that the proposed high-order directional features, when combined with the firstorder directional features (8-directional features), can improve the recognition accuracy, and they are more suitable for IAHCCR than traditional OHCCR. Additionally, the results also demonstrate the LSRC is a good choice for IAHCCR.