HISTOGRAM BASED CHARACTER RECOGNITION USING ARTIFICIAL NEURAL NETWORK

Abstract

Character recognition is the translation of handwritten or typewritten text into machine-editable form. 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can improve the interaction between man and machine in many applications, including office automation, preserving old/historical documents in electronic format and a large variety of banking, business and data entry applications. The task of isolated character recognition is a pattern classification problem, where an unknown input pattern is to be assigned to one out of a number of given classes. The main theme of this thesis is to prove that combined horizontal and vertical binary histograms could be used to recognize character using Neural Network. Histogram values of scanned normalized characters are treated as features for recognition. Computing histogram is a much simpler process as compared to other feature extraction methods; the proposed approach, therefore, needs less computational time as compared to Statistical methods, Structural Recognitions methods (strings and graphs matching) and template Matching.