Character-SIFT: A Novel Feature for Offline Handwritten Chinese Character Recognition
SIFT descriptor has been widely applied in computer vision and object recognition, but has not been explored in the field of handwritten Chinese character recognition. In this paper we proposed a novel SIFT based feature for offline handwritten Chinese character recognition. The presented feature is a modification of SIFT descriptor taking into account of the characteristics of handwritten Chinese samples. In our approach, global elastic meshing is first constructed and then the related gradient code of each sub-region is accumulated dynamically. Experiments using MQDF classifier show our feature’s effectiveness with a recognition rate of 97.868%, which outperforms original SIFT feature and two traditional features, Gabor feature and gradient feature.