In-air handwritten Chinese character recognition using multi-stage classifier based on adaptive discriminative locality alignment

The in-air handwriting is a natural and useful way for human-computer interaction. Yet, to our knowledge, few work has been done for the in-air handwritten Chinese character recognition (IAHCCR). In this paper, we present a multi-stage recognizer to address the problem of IAHCCR. The proposed methods can also deal with the classical handwritten Chinese character recognition (HCCR). We find that the discriminative locality alignment (DLA) technique in HCCR heavily depends on the choice of parameters in practice. To overcome the disadvantage, we present an adaptive discriminative locality alignment (ADLA), which does not involve the parameter optimization process. At the same time, a new static similar characters collection technique is proposed. We evaluate the proposed methods on the IAHCC-UCAS2014 dataset, an in-air handwritten Chinese character dataset constructed by us, as well as the SCUT-COUCH2009 database, a HCCR dataset. The experimental results demonstrate the effectiveness of the proposed methods on two different kinds of dataset.