Основные понятия
Introducing a robustness benchmark for Document Layout Analysis models, proposing metrics to evaluate perturbation impact, and presenting the RoDLA model for improved robust feature extraction.
Аннотация
The content introduces a robustness benchmark for Document Layout Analysis models, proposing metrics to evaluate perturbation impact. It presents the RoDLA model designed to enhance robust feature extraction. The study covers taxonomy of document perturbations, evaluation metrics, framework overview, and results on various datasets.
Introduction
Importance of Document Layout Analysis (DLA) in understanding documents.
Challenges posed by real-world document images due to quality variations.
Need for robustness testing in DLA models.
Perturbation Taxonomy
Hierarchical perturbations categorized into 5 groups with 12 types.
Description of perturbations like spatial transformation, content interference, inconsistency distortion, blur, and noise.
Perturbation Evaluation Metrics
Comparison of metrics like MS-SSIM, CW-SSIM, Degradation w.r.t baseline, and Mean Perturbation Effect (mPE).
Designing mPE metric to assess compound effects of document perturbations.
Robust Document Layout Analyzer
Framework overview of RoDLA model integrating channel attention and average pooling layers.
Design enhancements for robust feature extraction in RoDLA.
Benchmarking and Analysis
Results on PubLayNet-P dataset showcasing state-of-the-art performance by RoDLA.
Performance comparison with other methods on DocLayNet-P and M6Doc-P datasets.
Conclusion
Introduction of the first robustness benchmark for DLA models.
Proposal of two metrics - mPE and mRD - for evaluating perturbation impact and model robustness.
Статистика
"RoDLA method can obtain state-of-the-art performance on the perturbed and the clean data."
"RoDLA achieves a balanced profile with 70.0% in mAP on clean data."
"RoDLA maintains high performance under various perturbations."
Цитаты
"Our RoDLA method effectively harnesses robust features."
"RoDLA surprisingly achieves state-of-the-art performance on the clean data."
"RoDLA can obtain robust performance."