An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings
This addresses the problem of recognizing numeral strings in documents, including historical ones, for applications in digitization and automation, though it appears incremental as it builds on existing object detection methods.
The paper tackles handwritten numeral string recognition by proposing an end-to-end approach that uses a YoLo-based model to detect and recognize numerals, avoiding heuristic preprocessing and segmentation. Results show it is a feasible solution that reduces task complexity by eliminating classical steps like segmentation and length-specific classifiers.
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 A robust experimental protocol based on several numeral string datasets, including one composed of historical documents, has shown that the proposed method is a feasible end-to-end solution for numeral string recognition.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.