Automatic quality measurement of gray-scale handwriting based on extended average entropy

With a surge of interest in OCR in 1990s, a large number of handwriting or handprinting databases have been built one after another around the world. One problem that researches encounter today is that all the databases differ in various ways including the script qualities. This paper proposes a method for measuring handwriting qualities that can be used for comparison of databases and objective test for character recognizers. The key idea involved is classifying character samples into a number of groups each characterizing a set of qualities. In order to evaluate the proposed method, we carried out experiments on KU-1 database. The result we achieve is meaningful and the method is helpful for the target tasks.