Recognition of Online Isolated Handwritten Characters by Backpropagation Neural Nets Using Sub-Character Primitive Features

In online handwriting recognition, existing challenges are to cope with problems of various writing fashions, variable size for the same character, different stroke orders for the same letter, and efficient data presentation to the classifier. The similarities of distinct character shapes and the ambiguous writing further complicate the dilemma. A solitary solution of all these problems lies in the intelligent and appropriate extraction of features from the character at the time of writing. A typical handwriting recognition system focuses on only a subset of these problems. The goal of fully unconstrained handwriting recognition still remains a challenge due to the amount of variations found in characters. The handwriting recognition problem can be considered for various alphabets and at various levels of abstraction. The main goal of the work presented in this paper has been the development of an on-line handwriting recognition system which is able to recognize handwritten characters of several different writing styles. Due to the temporal nature of online data, this work has possible application to the domain of speech recognition as well. The work in this research aimed to investigate various features of handwritten letters, their use and discriminative power, and to find reliable feature extraction methods, in order to recognize them. A 22 feature set of sub-character primitive features has been proposed using a quite simple approach of feature extraction. This approach has succeeded in having robust pattern recognition features, while maintaining feature´s domain space to a small, optimum quantity. Backpropagation neural network (BPN) technique has been used as classifier and recognition rate up to 87% has been achieved even for highly distorted handwritten characters