  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.
