Recognition of an Indian Script Using Multilayer Perceptrons and Fuzzy Features

We present a multi-stage character recognition system for an Indian script, namely, Bengali (also called Bangla) using fuzzy features and multilayer perceptrons (MLP). The fuzzy features are extracted from Hough transform of a character pattern pixels. We first define a number of fuzzy sets on the Hough transform accumulator cells. The fuzzy sets are then combined by t-norms to generate feature vectors from each character. A set of fuzzy linguistic vectors is next generated from these feature vectors. The MLPs used for classification have the fuzzy features as inputs. The MLP outputs also represent the belongingness of an input pattern to different fuzzy character pattern classes. To improve the recognition accuracy of Bengali characters, we divide all the patterns into three distinct sets. Each set of characters is once again divided into a number of mutually exclusive character pattern classes. During recognition, the class of each pattern is first determined, followed by recognition of the actual character within that class. Recognition accuracy of the system is more than 98%.