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Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild by Luo, Canjie; Lin, Qingxiang; Liu, Yuliang; Jin, Lianwen; Shen, Chunhua is a scholarly article available to read on EtoBox.

What is Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild about?

We propose to improve text recognition from a new perspective by separating the text content from complex backgrounds. As vanilla GANs are not sufficiently robust to generate sequence-like characters in natural images, we propose an adversarial learning framework for the generation and recognition of multiple characters in an image. The proposed framework consists of an attention-based recognizer and a generative adversarial architecture. Furthermore, to tackle the issue of lacking paired training samples, we design an interactive joint training scheme, which shares attention masks from the recognizer to the discriminator, and enables the discriminator to extract the features of each character for further adversarial training. Benefiting from the character-level adversarial training, our framework requires only unpaired simple data for style supervision. Each target style sample containing only one randomly chosen character can be simply synthesized online during the training. This is significant as the training does not require costly paired samples or character-level annotations. Thus, only the input images and corresponding text labels are needed. In addition to the style normal

Author

Luo, Canjie; Lin, Qingxiang; Liu, Yuliang; Jin, Lianwen; Shen, Chunhua

Published

2020

Language

EN