This paper presents segmentation-free strategies for the recognition of handwritten numeral strings of unknown length. A synthetic dataset of touching numeral strings of sizes 2-, 3- and 4-digits was created to train end-to-end solutions based on Convolutional Neural Networks. A robust experimental protocol is used to show that the proposed segmentation-free methods may reach the state-of-the-art performance without suffering the heavy burden of over-segmentation based methods. In addition, they confirmed the importance of introducing contextual information in the design of end-to-end solutions, such as the proposed length classifier when recognizing numeral strings.
@article{arxiv.1804.09279,
title = {Segmentation-Free Approaches for Handwritten Numeral String Recognition},
author = {Andre G Hochuli and Luiz E S Oliveira and Alceu S Britto and Robert Sabourin},
journal= {arXiv preprint arXiv:1804.09279},
year = {2018}
}