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