Abstract: In order to recognize low-quality characters with interrupted strokes,noise and fuzziness,and to recog- nize characters with different fonts and sizes,a method to recognize low-quality characters on the basis of dictionary learning and sparse representation is proposed.Firstly,character samples with different fonts and sizes are collected to construct a super-complete dictionary of characters.Then,a sparse representation model is established by using test characters,and a character classification is made according to the solved sparse representation coefficient.Ad- ditionally,in order to make the dictionary more discriminating,a dictionary learning method on the basis of factor analysis is proposed.Experimental results show that the proposed method not only can identify characters with different fonts and sizes but also possesses robustness to interrupted strokes,noise and fuzziness.

Key words: character recognition, dictionary learning, sparse representation, factors analysisanalysis