# Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild

arXiv: https://arxiv.org/abs/2001.04189 · HTML: https://mlsanity.com/paper/2001.04189/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 14 of 14)

- [Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study](https://mlsanity.com/paper/2112.15093/) (2021)
- [Revisiting Scene Text Recognition: A Data Perspective](https://mlsanity.com/paper/2307.08723/) (2023)
- [CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition](https://mlsanity.com/paper/2111.11011/) (2021)
- [SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text](https://mlsanity.com/paper/2202.11456/) (2022)
- [SimAN: Exploring Self-Supervised Representation Learning of Scene Text via Similarity-Aware Normalization](https://mlsanity.com/paper/2203.10492/) (2022)
- [Unsupervised Structure-Texture Separation Network for Oracle Character Recognition](https://mlsanity.com/paper/2205.06549/) (2022)
- [SGBANet: Semantic GAN and Balanced Attention Network for Arbitrarily Oriented Scene Text Recognition](https://mlsanity.com/paper/2207.10256/) (2022)
- [Class-Aware Mask-Guided Feature Refinement for Scene Text Recognition](https://mlsanity.com/paper/2402.13643/) (2024)
- [Pure Transformer with Integrated Experts for Scene Text Recognition](https://mlsanity.com/paper/2211.04963/) (2022)
- [Intelligent Artistic Typography: A Comprehensive Review of Artistic Text Design and Generation](https://mlsanity.com/paper/2407.14774/) (2024)
- [PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition](https://mlsanity.com/paper/2207.14807/) (2022)
- [Searching a High-Performance Feature Extractor for Text Recognition Network](https://mlsanity.com/paper/2209.13139/) (2022)
- [Disentangled Generation Network for Enlarged License Plate Recognition and A Unified Dataset](https://mlsanity.com/paper/2206.00859/) (2022)
- [OTSNet: A Neurocognitive-Inspired Observation-Thinking-Spelling Pipeline for Scene Text Recognition](https://mlsanity.com/paper/2511.08133/) (2025)

## Abstract (arXiv)

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 normalization of the backgrounds, we refine character patterns to ease the recognition task. A feedback mechanism is proposed to bridge the gap between the discriminator and the recognizer. Therefore, the discriminator can guide the generator according to the confusion of the recognizer, so that the generated patterns are clearer for recognition. Experiments on various benchmarks, including both regular and irregular text, demonstrate that our method significantly reduces the difficulty of recognition. Our framework can be integrated into recent recognition methods to achieve new state-of-the-art recognition accuracy.