# CG-DIQA: No-reference Document Image Quality Assessment Based on Character Gradient

arXiv: https://arxiv.org/abs/1807.04047 · HTML: https://mlsanity.com/paper/1807.04047/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [DeQA-Doc: Adapting DeQA-Score to Document Image Quality Assessment](https://mlsanity.com/paper/2507.12796/) (2025)
- [RealityBench: Simulated Environments to Evaluate a Vision Language Model's Ability to Document Human Physical Work](https://mlsanity.com/paper/2610.14397/) (2026)

## Abstract (arXiv)

Document image quality assessment (DIQA) is an important and challenging problem in real applications. In order to predict the quality scores of document images, this paper proposes a novel no-reference DIQA method based on character gradient, where the OCR accuracy is used as a ground-truth quality metric. Character gradient is computed on character patches detected with the maximally stable extremal regions (MSER) based method. Character patches are essentially significant to character recognition and therefore suitable for use in estimating document image quality. Experiments on a benchmark dataset show that the proposed method outperforms the state-of-the-art methods in estimating the quality score of document images.