Prototyping structural description using an inductive learning program

Character recognition systems can contribute tremendously to the advancemen t of the automation process and can improve the interaction between man and machine in many applications, including office automation, check verificat ion and a large variety of banking, business and data entry applications. T he main theme of this paper is the automatic recognition of hand printed Ar abic characters using machine learning. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalize over the large degree of variation bet ween writing styles and recognition rules can be constructed by example. The system was tested on a sample of handwritten characters from several in dividuals whose writing ranged from acceptable to poor in quality and the c orrect average recognition rate obtained using cross-validation was 89.65%.