Neural Nets for On-line Isolated Handwritten Character Recognition: A Comparative Study

Abstract

Handwriting processing is a domain in great expansion which in the present day begins to see several industrial realizations. The field of personal computing has begun to make a transition from the desktop to handheld devices, thereby requiring input paradigms that are more suited for single hand entry than a keyboard. Online handwriting recognition allows for such input modalities. Handwriting recognition has always been a tough problem because of the handwriting variability, ambiguity and illegibility. This paper describes a simple approach involved in online handwriting recognition. Conventionally, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. The whole process requires no preprocessing and size normalization. The method is applicable for off-line character recognition as well. This is a writer-independent system based on two neural net (NN) techniques: back propagation neural network (BPN) and counter propagation neural network (CPN). Performances of BPN and CPN are tested for upper-case English alphabets for a number of different styles from different peoples.