# Manifold Mixup improves text recognition with CTC loss

## TL;DR

This paper introduces an adaptation of Manifold Mixup for handwritten text recognition using CTC loss, demonstrating improved accuracy across multiple languages and datasets.

## Contribution

It adapts Manifold Mixup for CTC-based text recognition, enhancing data augmentation techniques in this domain.

## Key findings

- Improved recognition accuracy on various datasets
- Effective adaptation of Mixup for CTC loss
- Enhanced performance across multiple languages

## Abstract

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.