  Unconstrained handwritten text recognition remains an important challenge for
deep neural networks. These last years, recurrent networks and more
specifically Long Short-Term Memory networks have achieved state-of-the-art
performance in this field. Nevertheless, they are made of a large number of
trainable parameters and training recurrent neural networks does not support
parallelism. This has a direct influence on the training time of such
architectures, with also a direct consequence on the time required to explore
various architectures. Recently, recurrence-free architectures such as Fully
Convolutional Networks with gated mechanisms have been proposed as one possible
alternative achieving competitive results. In this paper, we explore
convolutional architectures and compare them to a CNN+BLSTM baseline. We
propose an experimental study regarding different architectures on an offline
handwriting recognition task using the RIMES dataset, and a modified version of
it that consists of augmenting the images with notebook backgrounds that are
printed grids.
