Methods, reports and survey for the comparison of diverse isolated character recognition results on the UNIPEN database

Abstract

A framework of data organization methods and corresponding recognition results for UNIPEN databases is presented to enable the comparison of recognition results from different isolated character recognizers. A reproducible method for splitting the Train-R01/V07 data into an array of multi-writer and omni-writer training and testing pairs is proposed. Recognition results and uncertainties are provided for each pair, as well as results for the DevTest-R01/V02 character subsets, using an online scanning n-tuple recognizer. Several other published results are surveyed within this context. In sum, this report provides the reader multiple points of reference useful for comparing a number of published recognition results and a proposed framework that similarly allows private evaluation of unpublished recognition results.