Yoruba Handwriting Word Recognition Quality Evaluation of Preprocessing Attributes using Information Theory Approach

Abstract

This paper presents an approach to evaluate the quality of handwritten words using the set of features in the preprocessing stages. This is to determine the effect of various stages of preprocessing of the recognition of Yoruba handwritten characters. We demonstrate our methods using handwritten words samples of the domain of Yoruba medical terminology collected from indigenous Yoruba literate writers. Samples were digitized at 300dpi to facilitate much detail representation of the image dataset. We study the impact of entropy measure for an intuitive interpretation of the analysis of the handwritten words. From the experiment carried out, it was observed that the entropy measure of handwritten word is higher than the typewritten word. This implies that the Information content of the handwritten word is affected by perturbations which need to be removed using appropriate image preprocessing tools to obtain low entropy measure which implies same information content as the original. General Terms Pattern Recognition, Handwritten word recognition, Image preprocessing and Information Theory.