  Modern handwritten text recognition techniques employ deep recurrent neural
networks. The use of these techniques is especially efficient when a large
amount of annotated data is available for parameter estimation. Data
augmentation can be used to enhance the performance of the systems when data is
scarce. Manifold Mixup is a modern method of data augmentation that meld two
images or the feature maps corresponding to these images and the targets are
fused accordingly. We propose to apply the Manifold Mixup to text recognition
while adapting it to work with a Connectionist Temporal Classification cost. We
show that Manifold Mixup improves text recognition results on various languages
and datasets.
