Recognition of hand-printed latin characters based on a structural approach with a neural network classifier

Abstract

Character recognition systems can contribute tremendously to the advancement of the automation process and can improve the interaction between person and machine in many applications, including office automation, check verification, and a large variety of banking, business, and data entry applications. Our main theme is the automatic recognition of hand-printed Latin characters using artificial neural networks in combination with conventional techniques. This approach has a number of advantages: it combines rule-based (structural) and classification tests; it is more efficient for large and complex sets; and feature extraction is inexpensive and execution time is independent of handwriting style and size. The technique can be divided into three major steps. The first step is preprocessing in which the original image is transformed into a binary image utilizing a 300 dpi scanner and then thinned using a parallel thinning algorithm. Second, the image skeleton is traced from left to right to build a binary tree. Some primitives, such as straight lines, curves, and loops are extracted from the binary tree. Finally, a three layer artificial neural network is used for character classification. The system was tested on a sample of handwritten characters from several individuals whose writing ranged from acceptable to poor in quality and the correct recognition rate obtained was 91%.