# TedEval: A Fair Evaluation Metric for Scene Text Detectors

arXiv: https://arxiv.org/abs/1907.01227 · HTML: https://mlsanity.com/paper/1907.01227/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 3 of 3)

- [Towards End-to-End Unified Scene Text Detection and Layout Analysis](https://mlsanity.com/paper/2203.15143/) (2022)
- [Bharat Scene Text: A Novel Comprehensive Dataset and Benchmark for Indian Language Scene Text Understanding](https://mlsanity.com/paper/2511.23071/) (2025)
- [How Do Document Parsers Break? Auditing Structural Vulnerability in Document Intelligence](https://mlsanity.com/paper/2605.19309/) (2026)

## Abstract (arXiv)

Despite the recent success of scene text detection methods, common evaluation metrics fail to provide a fair and reliable comparison among detectors. They have obvious drawbacks in reflecting the inherent characteristic of text detection tasks, unable to address issues such as granularity, multiline, and character incompleteness. In this paper, we propose a novel evaluation protocol called TedEval (Text detector Evaluation), which evaluates text detections by an instance-level matching and a character-level scoring. Based on a firm standard rewarding behaviors that result in successful recognition, TedEval can act as a reliable standard for comparing and quantizing the detection quality throughout all difficulty levels. In this regard, we believe that TedEval can play a key role in developing state-of-the-art scene text detectors. The code is publicly available at https://github.com/clovaai/TedEval.