An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings

An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings

Hochuli, Andre G. and Britto, Alceu S. and Barddal, Jean P. and Sabourin, Robert and Oliveira, Luiz E.S.

Proceedings of the International Joint Conference on Neural Networks 2020

Abstract : An end-to-end solution for handwritten numeral string recognition is proposed, in which the numeral string is considered as composed of objects automatically detected and recognized by a YoLo-based model. The main contribution of this paper is to avoid heuristic-based methods for string preprocessing and segmentation, the need for task-oriented classifiers, and also the use of specific constraints related to the string length. A robust experimental protocol based on several numeral string datasets, including one composed of historical documents, has shown that the proposed method is a feasible end-to-end solution for numeral string recognition. Besides, it reduces the complexity of the string recognition task considerably since it drops out classical steps, in special preprocessing, segmentation, and a set of classifiers devoted to strings with a specific length.