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In this paper, we introduce WeLayout, a novel system for segmenting thelayout of corporate documents, which stands for WeChat Layout Analysis System.Our approach utilizes a sophisticated ensemble of DINO and YOLO models,specifically developed for the ICDAR 2023 Competition on Robust LayoutSegmentation. Our method significantly surpasses the baseline, securing a topposition on the leaderboard with a mAP of 70.0. To achieve this performance, weconcentrated on enhancing various aspects of the task, such as datasetaugmentation, model architecture, bounding box refinement, and model ensembletechniques. Additionally, we trained the data separately for each documentcategory to ensure a higher mean submission score. We also developed analgorithm for cell matching to further improve our performance. To identify theoptimal weights and IoU thresholds for our model ensemble, we employed aBayesian optimization algorithm called the Tree-Structured Parzen Estimator.Our approach effectively demonstrates the benefits of combining query-based andanchor-free models for achieving robust layout segmentation in corporatedocuments.

WeLayout: WeChat Layout Analysis System for the ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents