RECOGNITION OF HAND-PRINTED LATIN CHARACTERS BASED ON GENERALIZED HOUGH TRANSFORM AND DECISION TREE LEARNING TECHNIQUES

Abstract

This paper presents a new technique for the recognition of hand-printed Latin characters using machine learning. Conventional methods have relied on manually constructed dictionaries which are not only tedious to construct but also difficult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalize over a large degree of variation between writing styles, and recognition rules can be constructed by example. Characters are scanned into the computer and preprocessing techniques transform the bit-map representation of the characters into a set of primitives which can be represented in an attribute base form. A set of such representations for each character is then input to C4.5 which produces a decision tree for classifying each character.