Category-dependent feature extraction for recognition of degraded handwritten characters

Conventional methods for recognizing multiple fonts and handwriting are generally robust against deformation, but are weak against degradation. This paper proposes a category-dependent feature extraction method that resists both deformation and degradation. Our proposed method compares an input pattern with the template of each category and estimates the degree of degradation of the input pattern. Approximate stroke run-lengths without degradation are then obtained by compensating the inaccurate runs caused by degradation. Recognition experiments using degraded handwritten characters show that the proposed feature is superior to conventional ones in resisting degradation.