Neural network recognition of hand-printed characters
Character recognition systems can contribute tremendously to the advancement of the automation process, and can improve the interaction between man and machine in many applications, including office automation, cheque verification and a large variety of banking business and data entry applications. The main theme of this paper is the automatic recognition of hand-printed Latin characters using a; artificial neural networks in combination with conventional techniques. This approach has a number of advantages: it combines rule-based (structural) approach for feature extraction and nonlinear classification tests for recognition; it is more efficient for large and complex data sets; 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 pre-processing in which the original image is transformed into a binary image utilising a 300 dpi scanner and then thinned using a parallel thinning algorithm. Second, the image-skeleton is traced from left to right in order 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 average recognition rate obtained using cross-validation was 86%.
neural network hand-printed characters feature extraction parallel thinning back-propagation pattern recognition structural classification