Prediction of handwriting legibility

This paper describes an independent handwriting style classifier that has been designed to select the best recognizer for a given style of writing. For this purpose a definition of handwriting legibility has been defined and a method has been implemented that can predict this legibility. The technique consists of two phases. In the feature extraction phase, a set of 16 features is extracted from the image contour. These features have been selected from amongst a set of pre-recognition features as those features that contribute the most (95%) to a discriminant between legible and illegible words. In the classification phase, a Probability Neural Network based on Bayesian decision is introduced to predict the legibility of unknown handwriting using a Parzen method to estimate a class conditional density function from the available training data.