Are Sparse Representation and Dictionary Learning Good for Handwritten Character Recognition?

Recently the theories of sparse representation (SR) and dictionary learning (DL) have brought much attention and become powerful tools for pattern recognition and computer vision. Due to the fact that images can be represented in a sparse and compressible way with respect to some dictionaries, these theories have shown successful applications in many different areas including face recognition, image denoising and in painting, medical imaging, image classification and registration, motion estimation, and many more. Over a relatively short time, many improvements and innovative ideas using SR and DL have been developed. However, very little published work is found in the application of these theories on handwritten character recognition. One question comes to mind is whether these theories could produce good results for handwritten character recognition as in the case of other applications. In this paper, we would like to address this question by investigating various applications of the theories to handwritten character recognition. Experiments were conducted in both handwritten digits and alphabetical characters on three benchmark databases: MNIST, USPS, and CEDAR. The results showed that while this approach can achieve good results, it cannot beat the state of the art. The main advantage of this approach is that it does not require the choice of features and hence it may reduce computational cost.