Recognition of handwritten characters has been a popular task for the evaluation of classification algorithms for many years. Looking at the latest results on databases such as USPS or MNIST, one could think that character recognition is a solved problem. In this paper, we claim that this is not the case for two reasons : first because the classical databases for digit recognition are realistic but too simple and second because digit recognition is not a real-world task but only a part of it. In this paper, we contribute to a better understanding of these two aspects with new results. In a first part, we compare three state-of-the-art recognizers on a digit recognition task extracted from a real world application and show that the error rates on this database can not be extrapolated from MNIST. Then, in a second part, we present and evaluate a system designed for an industrial application based on character recognition : document identification with floating field recognition.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.