  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.
