  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composed of historical documents, has shown that the proposed method is a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.
