Manifold Mixup Improves Text Recognition with CTC Loss 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 with a regularization effect 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 consistently improves text recognition results on various languages and datasets.

Fast Distributional Smoothing for Regularization in CTC Applied to Text Recognition Many recent text recognition studies achieved successful performance by applying a sequentiallabel prediction framework such as connectionist temporal classification. Meanwhile, regularization is known to be essential to avoid overfitting when training deep neural networks. Regularization techniques that allow for semisupervised learning have a greater impact than those that do not. Among widely researched singlelabel regularization techniques, virtual adversarial training (VAT) performs successfully by smoothing posterior distributions around training data points. However, VAT is almost solely applied to singlelabel prediction tasks, not to sequentiallabel prediction tasks. This is because the number of possible candidates in the label sequence exponentially increases with the sequence length, making it impractical to calculate posterior distributions and the divergence between them. Investigating this problem, we have found that there is an easily computable upper bound for divergence. Here, we propose fast distributional smoothing (FDS) as a method for drastically reducing computational costs by minimizing this upper bound. FDS allows regularization at practical computational costs in both supervised and semisupervised learning. An experiment under simple settings confirmed that upperbound minimization decreases divergence. Experiments also show that FDS improves scene text recognition performance and enhances stateoftheart regularization performance. Furthermore, experiments show that FDS enables efficient semisupervised learning in sequentiallabel prediction tasks and that it outperforms a conventional semisupervised method.