In-air Handwritten Chinese character recognition using Discriminative Projection based on Locality-sensitive Sparse Representation

Dimensionality reduction methods have been shown to be effective for handwritten Chinese character recognition. In this paper, we propose discriminative projection based on localitysensitive sparse representation (DPLSR) for in-air handwritten Chinese character recognition. DPLSR based on the localitysensitive sparse representation based classifier (LSRC), which can provide closed-form solutions and maintain the data locality constraint during the sparse coding stage. In contrast to sparse representation classifier steered discriminative projection (SRCDP), which did not consider global structure of data and use all training samples as dictionary atoms, DPLSR is able to use fewer atoms and spend less training time to achieve better performance. Experiments are conducted on the IAHCC-UCAS2016 dataset built by us, experimental results demonstrate the effectiveness of proposed method.