Neural Network Based Recognition of Machine Printed Hangul Characters of Low Quality

In this paper, we propose a Hangul character recognition method in which new letter components as recognition units are introduced and the MLP (multilayer perceptrons) neural networks are employed for two-step recognition of Hangul. To recognize Hangul character, we divide it into two or three recognition units and extract the direction angle features of them to be fed to the corresponding neural network recognizers. The recognition results of neural network recognizers are combined by another neural network. The experiments were conducted on the Hangul characters from real letter envelopes which are collected in the mail centers in Korea and the results showed that our method performs better than the conventional one.