Laser Print Document Identification Based on Gabor Feature and Sparse Representation Classification

In order to automatically identify laser print documents, a new sparse representation algorithm based on Gabor features is proposed for print document identification. Considering that toner accumulation texture characteristics of laser print documents, the proposed method first extracts Gabor features of the image at multiple scales and multiple orientations, and then uses principal component analysis to reduce the Gabor feature dimension. At last, different classifiers are used to the identification of laser print documents. Experimental results on our database show its efficiency and effectiveness with a correct printer identification rate of 94.74%.