Prediction of handwriting legibility

Author

Dehkordi, Mandana Ebadian ; Sherkat, Nasser ; Allen, Tony

Author_Institution

Dept. of Comput., Nottingham Univ., UK

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

997

Lastpage

1001

Abstract

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

Keywords

belief networks; handwriting recognition; neural nets; probability; Bayesian decision; Parzen method; feature extraction phase; handwriting legibility prediction; image contour; independent handwriting style classifier; linear discriminant function; probability neural network; Bayesian methods; Density functional theory; Electronic mail; Feature extraction; Handwriting recognition; Humans; Iris; Neural networks; Phase estimation; Writing;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on

Conference_Location

Seattle, WA

Print_ISBN

0-7695-1263-1

Type

conf

DOI

10.1109/ICDAR.2001.953935

Filename

953935