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arXiv · 1804.09279

Segmentation-Free Approaches for Handwritten Numeral String Recognition

Andre G Hochuli·Luiz E S Oliveira·Alceu S Britto Jr·Robert Sabourin
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Abstract

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.

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Andre G Hochuli, Luiz E S Oliveira, Alceu S Britto Jr, Robert Sabourin. 2018-04-24. Segmentation-Free Approaches for Handwritten Numeral String Recognition. https://arxiv.org/abs/1804.09279

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