You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

The advent of recurrent neural networks for handwriting recognition marked an important
milestone reaching impressive recognition accuracies despite the great variability that we …

Handwritten Text Recognition has achieved an impressive performance in public
benchmarks. However, due to the high inter-and intra-class variability between handwriting …

Nowadays, deep learning methods are employed in a broad range of research fields. The
analysis and recognition of historical documents, as we survey in this work, is not an …

Deep convolutional features for word images and textual embedding schemes have shown
great success in word spotting. In this work, we follow these motivations to propose an …

Abstract Before develo** a Document Layout Analysis (DLA) model in real-world
applications conducting comprehensive robustness testing is essential. However the …

Abstract Handwritten Text Recognition (HTR) is still a challenging problem because it must
deal with two important difficulties: the variability among writing styles, and the scarcity of …

We address the problem of offline handwritten diagram recognition. Recently, it has been
shown that diagram symbols can be directly recognized with deep learning object detectors …

Script recognition has many real-life applications like optical character recognition,
document archiving, writer identification, searching within the documents, etc. Automatic …

From the early days of pattern recognition, word spotting have been important test beds for
studying how well machines can perform better decision making. In recent years, word …