Generating Text Sequence Images for Recognition

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.

References in corpus (12)

• Adam: A Method for Stochastic Optimization
• Conditional Generative Adversarial Nets
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Improved Techniques for Training GANs
• Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
• Focusing Attention: Towards Accurate Text Recognition in Natural Images
• Synthetic Data for Text Localisation in Natural Images
• Recursive Recurrent Nets with Attention Modeling for OCR in the Wild
• Edit Probability for Scene Text Recognition
• Reading Text in the Wild with Convolutional Neural Networks
• Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4
• Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes