(untitled) (International Journal of Engineering and Computer Science, 2017)
Recognition rate of handwritten English character is still limited due to presence of large variation of shape, scale and format in hand written characters. The thing that’s very difficult to deal with in character recognition is that the handwriting of a person differs from one person to another and considering the human error it is impossible for one person to write the same thing over and over again where it has to be the exact writing. However this is considered individualistic where the consistent individual features are hidden in the character handwriting. For that reason this study going to focus on the Off-Line English language characters in order to extract geometric moment’s features for the Characters shape of the Handwriting recognition. The geometric moment’s features are being completed thoroughly also the presence of solo features will be legitimized by checking and investigating it granularly; therefore the idea of applying the Invariant Discretization. By injecting the solo performance to the system through the injection of different issues for the solo feature into individual feature or standard performance this is being accomplished by the support of Invariant Discretization. Where the advantage of the Invariant Discretization to reduce the similarity error for intra-class (of the same character), with the increase of the similarity error for inter-class (of different characters)in recognition of Off-Line handwritten English characters with Fuzzy logic .