Enhancing document dewarping evaluation: A new metric with improved accuracy and efficiency

Recently, the task of document image dewarping has garnered significant attention. With the development of a series of advanced models, the performance on various benchmark datasets has seen considerable improvement, as evidenced by the increasingly better quantitative outcomes. However, several recent studies have unveiled that the commonly used evaluation metrics may not consistently represent the dewarping performance, leading to discrepancies between evaluation results and human perceptual judgments. While some alternative metrics have been recently proposed, their efficacy has not been fully validated, and we found that their performance remains suboptimal. To address these issues, we propose a new metric, termed DocAligner Distortion (DD), to mitigate the deficiencies observed in existing metrics. We conduct comprehensive comparisons and analyses between DD and the prevailing metrics used in document image dewarping. Results demonstrate that DD significantly outperforms its predecessors with better accuracy and efficiency. Codes are available at https://github.com/ZZZHANG-jx/DocAligner-Distortion.