# Boosting Optical Character Recognition: A Super-Resolution Approach

arXiv: https://arxiv.org/abs/1506.02211 · HTML: https://mlsanity.com/paper/1506.02211/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2015.

## Built on by (most cited first, 19 of 19)

- [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)
- [Learning Generative Structure Prior for Blind Text Image Super-resolution](https://mlsanity.com/paper/2303.14726/) (2023)
- [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)
- [One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer](https://mlsanity.com/paper/2308.02770/) (2023)
- [Enhanced Generative Structure Prior for Chinese Text Image Super-resolution](https://mlsanity.com/paper/2508.07537/) (2025)
- [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)
- [An end-to-end Optical Character Recognition approach for ultra-low-resolution printed text images](https://mlsanity.com/paper/2105.04515/) (2021)
- [Efficient scene text image super-resolution with semantic guidance](https://mlsanity.com/paper/2403.13330/) (2024)
- [Task-driven single-image super-resolution reconstruction of document scans](https://mlsanity.com/paper/2407.08993/) (2024)
- [NCAP: Scene Text Image Super-Resolution with Non-CAtegorical Prior](https://mlsanity.com/paper/2504.00410/) (2025)
- [Task-driven real-world super-resolution of document scans](https://mlsanity.com/paper/2506.06953/) (2025)

## Abstract (arXiv)

Text image super-resolution is a challenging yet open research problem in the computer vision community. In particular, low-resolution images hamper the performance of typical optical character recognition (OCR) systems. In this article, we summarize our entry to the ICDAR2015 Competition on Text Image Super-Resolution. Experiments are based on the provided ICDAR2015 TextSR dataset and the released Tesseract-OCR 3.02 system. We report that our winning entry of text image super-resolution framework has largely improved the OCR performance with low-resolution images used as input, reaching an OCR accuracy score of 77.19%, which is comparable with that of using the original high-resolution images 78.80%.