Relaxation-based pattern matching using automatic differentiation for off-line character recognition

Abstract

The paper describes a relaxation based matching method for offline character recognition. This method employs elastic stroke models as standard character patterns. Pattern similarity between a standard and an input pattern is defined by fuzzy logic. The matching process is formalized as a maximization problem of the similarity and computed by the steepest descent technique. To implement this technique, we adopted automatic differentiation, which made it possible to calculate the partial derivatives of the target function automatically, only given the definition of that function. Results of computer experiments targeting 46 hiragana characters from the ETL8B database revealed a maximum recognition rate of 98.8% for 20 input sets when combining stroke springs with relative location springs.