# TextSR: Content-Aware Text Super-Resolution Guided by Recognition

arXiv: https://arxiv.org/abs/1909.07113 · HTML: https://mlsanity.com/paper/1909.07113/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2019.

## Built on by (most cited first, 26 of 26)

- [Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study](https://mlsanity.com/paper/2112.15093/) (2021)
- [Text Prior Guided Scene Text Image Super-resolution](https://mlsanity.com/paper/2106.15368/) (2021)
- [A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution](https://mlsanity.com/paper/2203.09388/) (2022)
- [C3-STISR: Scene Text Image Super-resolution with Triple Clues](https://mlsanity.com/paper/2204.14044/) (2022)
- [Diffusion-based Blind Text Image Super-Resolution](https://mlsanity.com/paper/2312.08886/) (2023)
- [Visual Text Processing: A Comprehensive Review and Unified Evaluation](https://mlsanity.com/paper/2504.21682/) (2025)
- [A Benchmark for Chinese-English Scene Text Image Super-resolution](https://mlsanity.com/paper/2308.03262/) (2023)
- [Scene Text Image Super-resolution based on Text-conditional Diffusion Models](https://mlsanity.com/paper/2311.09759/) (2023)
- [Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks](https://mlsanity.com/paper/2210.06924/) (2022)
- [Towards Robust Scene Text Image Super-resolution via Explicit Location Enhancement](https://mlsanity.com/paper/2307.09749/) (2023)
- [Visual Text Meets Low-level Vision: A Comprehensive Survey on Visual Text Processing](https://mlsanity.com/paper/2402.03082/) (2024)
- [Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders](https://mlsanity.com/paper/2506.04641/) (2025)
- [Text-Aware Image Restoration with Diffusion Models](https://mlsanity.com/paper/2506.09993/) (2025)
- [Visual-Semantic Transformer for Scene Text Recognition](https://mlsanity.com/paper/2112.00948/) (2021)
- [Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios](https://mlsanity.com/paper/2503.07232/) (2025)
- [One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer](https://mlsanity.com/paper/2308.02770/) (2023)
- [Restore Text First, Enhance Image Later: Two-Stage Scene Text Image Super-Resolution with Glyph Structure Guidance](https://mlsanity.com/paper/2510.21590/) (2025)
- [Unified Diffusion Transformer for High-fidelity Text-Aware Image Restoration](https://mlsanity.com/paper/2512.08922/) (2025)
- [Super-Resolution Information Enhancement For Crowd Counting](https://mlsanity.com/paper/2303.06925/) (2023)
- [MF-LPR2: Multi-Frame License Plate Image Restoration and Recognition using Optical Flow](https://mlsanity.com/paper/2508.14797/) (2025)
- [Task-driven single-image super-resolution reconstruction of document scans](https://mlsanity.com/paper/2407.08993/) (2024)
- [TextSR: Diffusion Super-Resolution with Multilingual OCR Guidance](https://mlsanity.com/paper/2505.23119/) (2025)
- [HiREN: Towards Higher Supervision Quality for Better Scene Text Image Super-Resolution](https://mlsanity.com/paper/2307.16410/) (2023)
- [One Model for Two Tasks: Cooperatively Recognizing and Recovering Low-Resolution Scene Text Images by Iterative Mutual Guidance](https://mlsanity.com/paper/2409.14483/) (2024)
- [Task-driven real-world super-resolution of document scans](https://mlsanity.com/paper/2506.06953/) (2025)
- [OTSNet: A Neurocognitive-Inspired Observation-Thinking-Spelling Pipeline for Scene Text Recognition](https://mlsanity.com/paper/2511.08133/) (2025)

## Abstract (arXiv)

Scene text recognition has witnessed rapid development with the advance of convolutional neural networks. Nonetheless, most of the previous methods may not work well in recognizing text with low resolution which is often seen in natural scene images. An intuitive solution is to introduce super-resolution techniques as pre-processing. However, conventional super-resolution methods in the literature mainly focus on reconstructing the detailed texture of natural images, which typically do not work well for text due to the unique characteristics of text. To tackle these problems, in this work, we propose a content-aware text super-resolution network to generate the information desired for text recognition. In particular, we design an end-to-end network that can perform super-resolution and text recognition simultaneously. Different from previous super-resolution methods, we use the loss of text recognition as the Text Perceptual Loss to guide the training of the super-resolution network, and thus it pays more attention to the text content, rather than the irrelevant background area. Extensive experiments on several challenging benchmarks demonstrate the effectiveness of our proposed method in restoring a sharp high-resolution image from a small blurred one, and show that the recognition performance clearly boosts up the performance of text recognizer. To our knowledge, this is the first work focusing on text super-resolution. Code will be released in https://github.com/xieenze/TextSR.