Building compact MQDF classifier for large character set recognition by subspace distribution sharing

Abstract

Quadratic classifier with modified quadratic discriminant function (MQDF) has been successfully applied to recognition of handwritten characters to achieve very good performance. However, for large category classification problem such as Chinese character recognition, the storage of the parameters for the MQDF classifier is usually too large to make it practical to be embedded in the memory limited hand-held devices. In this paper, we aim at building a compact and high accuracy MQDF classifier for these embedded systems. A method by combining linear discriminant analysis and subspace distribution sharing is proposed to greatly compress the storage of the MQDF classifier from 76.4 to 2.06 MB, while the recognition accuracy still remains above 97%, with only 0.88% accuracy loss. Furthermore, a two-level minimum distance classifier is employed to accelerate the recognition process. Fast recognition speed and compact dictionary size make the high accuracy quadratic classifier become practical for hand-held devices.