Recognition of handprinted Chinese characters by constrained graph matching

Abstract A model-based handwritten Chinese character recognition (HCCR) system is proposed. The characters are represented by attributed relational graphs (ARG) using strokes as ARG vertices. A number of vector relational attributes are also used in the representation to improve the performance of the translation and scale invariant and rotation sensitive recognition system. Since the ETL-8 database is very noisy and broken strokes are commonly encountered, a suitable homomorphic energy function is proposed that allows the segments of a broken stroke of a test character to be matched to the corresponding model stroke. The homomorphic ARG matching energy is minimised using the self-organising Hopfield neural networks [1] [Suganthan, P.N., Teoh, E.K., Mital, D.P., A self-organising Hopfield network for attributed relational graph matching, Image and Vision Computing, 13(1) (1995) 61–73]. An effective formulation is introduced to determine the matching score. The formulation does not penalise the matching scores of test characters with broken strokes. Experiments were performed with 100 classes of characters in the ETL-8 database and 98.9% recognition accuracy has been achieved.