Numerical recognition of unconstrained handwriting

Abstract

This paper investigates the collaboration of three numeral recognition techniques that will detect unconstrained hand-written numerals. Our research focuses on a feature-based approach, a method using eigenimages, and a structural analysis approach using wavelet transforms. These approaches will generate an estimate for the input numeral and a corresponding confidence value that in combination, yield a final decision. This paper shows that each individual method performs with a detection rate of better than 80%, and by combining the detection strategies, detection rates up to 94% are observed.