Robust measurement based handwriting recognition method and system

The invention discloses a robust measurement based handwriting identification method and system. The method comprises the steps of: constructing a weighted similar map by performing similarity learning on a handwriting training sample; and keeping local features of all training samples while compacting a local intra-class divergence and separating a local inter-class divergence. In order to improve robustness of handwriting description, 1-norm measurement is proposed to be applied in a semi-supervised learning model, so as to design a performance robustness handwriting identification method and system, and output a projection matrix P that can be used for handwriting image feature extraction within a sample and outside the sample. Induction on images other than the sample comprises the steps of: projecting a test sample to the projection matrix P to input an extracted feature into an effective label propagation classifier for classification; and selecting a place of maximum probability in a corresponding category of soft label to determine a category of the test sample, so as to obtain a most accurate character recognition result. Meanwhile, by establishing a ratio model, model parameters are reduced, and the projection matrix P meets the orthogonal property.