  In this paper, we introduce WeLayout, a novel system for segmenting the
layout 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 Layout
Segmentation. Our method significantly surpasses the baseline, securing a top
position on the leaderboard with a mAP of 70.0. To achieve this performance, we
concentrated on enhancing various aspects of the task, such as dataset
augmentation, model architecture, bounding box refinement, and model ensemble
techniques. Additionally, we trained the data separately for each document
category to ensure a higher mean submission score. We also developed an
algorithm for cell matching to further improve our performance. To identify the
optimal weights and IoU thresholds for our model ensemble, we employed a
Bayesian optimization algorithm called the Tree-Structured Parzen Estimator.
Our approach effectively demonstrates the benefits of combining query-based and
anchor-free models for achieving robust layout segmentation in corporate
documents.
