ABSTRACT
Handwriting has continued to persist as a means of communication and recording information in day-to-day life
even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of
handwriting has practical significance, as in reading handwritten notes in a personal Digital Assistant (PDA), in
postal addresses on envelopes, in amounts in bank checks, in handwritten fields, in forms etc. To solve the
problem of writer identification with intermediate classes (writers) and objects (characters) , it is a good way to
extract the features with clear physical meanings. The extracted features are in variant under translation scaling
and stroke width.The off-line (which pertains to scanned images) is considered. Algorithms of preprocessing,
character and word recognition, and performance with practical system are indicated. The recognition rate of
Radial Basis Function (RBF) is found to be better compared to that of Back Propagation Network (BPN). The
recognition rate in the proposed system lies between 90% to 100%.
Keywords: - Neural Network, writer identification, back propagation and Radial Basis Function (RBF)