Abstract Internet of Things (IoT) is widely accepted technology in both industrial as well as
academic field. The objective of IoT is to combine the physical environment with the cyber …

Text recognition is a long-standing research problem for document digitalization. Existing
approaches are usually built based on CNN for image understanding and RNN for char …

Handwritten Text Recognition (HTR) is essential for digitizing historical documents in
different kinds of archives. In this study, we introduce a hybrid form archive written in French …

Current state-of-the-art approaches to offline Handwritten Text Recognition extensively rely
on Multidimensional Long Short-Term Memory networks. However, these architectures …

Unconstrained handwritten text recognition is a challenging computer vision task. It is
traditionally handled by a two-step approach, combining line segmentation followed by text …

In this paper, we are interested in modeling complex activities that occur in a typical
household. We propose to use programs, ie, sequences of atomic actions and interactions …

The success of deep learning based models have centered around recent architectures and
the availability of large scale annotated data. In this work, we explore these two factors …

Unconstrained handwritten text recognition remains challenging for computer vision
systems. Paragraph text recognition is traditionally achieved by two models: the first one for …

The recognition of handwritten text is challenging as there are virtually infinite ways a human
can write the same message. Deep learning approaches for handwriting analysis have …

This paper proposes the use of a new neural network architecture that combines a deep
convolutional neural network with an encoder–decoder, called sequence to sequence, to …