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 A robust experimental protocol based on several numeral string datasets, including one composed of historical documents, has shown that the proposed method is a feasible end-to-end solution for numeral string recognition.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.

中文翻译：

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识别现代和历史手写数字字符串的端到端方法

提出了一种用于手写数字串识别的端到端解决方案，其中将数字串视为由基于 YoLo 的模型自动检测和识别的对象组成。本文的主要贡献是避免了基于启发式的字符串预处理和分割方法、面向任务的分类器的需要以及与字符串长度相关的特定约束的使用。基于多个数字串数据集（包括一个由历史文档组成的数据集）的稳健实验协议表明，所提出的方法是一种可行的端到端数字串识别解决方案。此外，它大大降低了字符串识别任务的复杂性，因为它在特殊的预处理、分割、