  Unconstrained handwritten text recognition is a major step in most document
analysis tasks. This is generally processed by deep recurrent neural networks
and more specifically with the use of Long Short-Term Memory cells. The main
drawbacks of these components are the large number of parameters involved and
their sequential execution during training and prediction. One alternative
solution to using LSTM cells is to compensate the long time memory loss with an
heavy use of convolutional layers whose operations can be executed in parallel
and which imply fewer parameters. In this paper we present a Gated Fully
Convolutional Network architecture that is a recurrence-free alternative to the
well-known CNN+LSTM architectures. Our model is trained with the CTC loss and
shows competitive results on both the RIMES and IAM datasets. We release all
code to enable reproduction of our experiments:
https://github.com/FactoDeepLearning/LinePytorchOCR.
