Offline English Hand Written Character Recognition Using Neural Network

Image processing and pattern recognition plays a lead role in handwritten character recognition. The recognition of handwriting can,however, still be considered an open research problem due to its substantial variation in appearance .There are four main steps of handwritten character recognition-Data collection and pre -processing, segmentation feature extraction and classification. The main objective of this research is to find a new solution for handwritten text recognition of different fonts and styles by improving the design structure of the feature extraction. The main aim of this paper is to propose a fast and easy to use feature extraction method that obtains a good performance. This study focuses on isolated characters. Diagonal feature extraction scheme for recognizing off-line handwritten characters is proposed in this work in addition efficient feature such as Eigen value an d mean value are also used which improves accuracy to recognize character. Diagonal features are playing an important role in order to achieve higher accuracy of the recognition system. It describes recent achievements, difficulties, successes and challen ges in all aspects of handwriting recognition. It also presents a new approach which dramatically improves current handwriting recognition systems.