From Isolated Handwritten Characters to Fields Recognition: There's Many a Slip Twixt Cup and Lip

Abstract

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.

A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits

“…Kermorvant et al [10] introduced an entirely new way of recognizing handwritten and offline character characters using a noncontinuous neural network. ey compared the results with the use of a locally based HMM system.…”