# Generating Text Sequence Images for Recognition

## TL;DR

This paper introduces a novel method using conditional adversarial networks to generate unlimited realistic text sequence images directly from semantic data, enhancing training data for text recognition.

## Contribution

It proposes a simple, end-to-end image-to-image translation approach for synthesizing text images without complex pre/post-processing steps.

## Key findings

- Generated images are realistic and diverse.
- The method produces high-quality training data.
- Evaluation metrics confirm the effectiveness of the generated images.

## Abstract

Recently, methods based on deep learning have dominated the field of text recognition. With a large number of training data, most of them can achieve the state-of-the-art performances. However, it is hard to harvest and label sufficient text sequence images from the real scenes. To mitigate this issue, several methods to synthesize text sequence images were proposed, yet they usually need complicated preceding or follow-up steps. In this work, we present a method which is able to generate infinite training data without any auxiliary pre/post-process. We tackle the generation task as an image-to-image translation one and utilize conditional adversarial networks to produce realistic text sequence images in the light of the semantic ones. Some evaluation metrics are involved to assess our method and the results demonstrate that the caliber of the data is satisfactory. The code and dataset will be publicly available soon.