Printed Odia Digit Recognition Using Finite Automaton

Abstract

Odia digit recognition (ODR) is one of the intriguing areas of research topic in the field of optical character recognition. This communication is an attempt to recognize printed Odia digits by considering their structural information as features and finite automaton with output as recognizer. The sample data set is created for Odia digits, and we named it as Odia digit database (ODDB). Each image is passed through several precompiled standard modules such as binarization, noise removal, segmentation, skeletonization. The image thus obtained is normalized to a size of 32 × 32 2D image. Chain coding is used on the skeletonised image to retrieve information regarding number of end points, $T$-joints, $X$-joints and loops. It is observed that finite automaton is able to classify the digits with a good accuracy rate except the digits . We have used the correlation function to distinguish between, . For our experiment we have considered some poor quality degraded printed documents. The simulation result records 96.08 % overall recognition accuracy.