Paper Title
Neural Network Implementation For Image Processing Using Handwritten Character Recognition

Abstract
Handwritten Character Classification (HCC) is the process of classifying handwritten characters into appropriate classes based on the features extracted from each character. Handwritten character classification can be performed either online or offline. A system has been developed for offline HCR of Devnagari writing systems using Nearest Neighbour Algorithm. A lot of people today are trying to write their own HCR (Handwritten Character Classification) system or to improve the quality of an existing one. This article shows how the use of Neural Network for development of an handwritten character application, while achieving highest rate of classification and good performance. There are three primary processes utilized in most character classification systems. The first is the representation process where giving the input as a character is to get an image of the character and then treated in different ways to achieve a higher level form of the data. First, the image should undergo some image enhancements such as cropping, reshaping, and filtering out noise, this is called image preprocessing. The raw digitized data is then mapped to a higher level by extracting special characteristics and patterns of the image. This is called feature extraction. Also the process of segmentation is also carried out to relate with the previous algorithm. The higher level image is then stored in some special way, perhaps in a vector