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.

翻译：本文介绍了WeLayout——一种用于企业文档版面分割的新颖系统，其全称为微信版面分析系统（WeChat Layout Analysis System）。本方法采用DINO与YOLO模型的精细集成方案，专为ICDAR 2023鲁棒版面分割竞赛而开发。我们的方法显著超越基线水平，以70.0的平均精度均值（mAP）位居排行榜首位。为实现此性能，我们重点优化了任务的多项环节，包括数据集增强、模型架构、边界框细化及模型集成技术。此外，我们针对各文档类别分别训练数据以确保更高的平均提交分数，并开发了单元格匹配算法以进一步提升性能。为确定模型集成的最优权重和IoU阈值，我们采用了名为树结构帕森估计器（Tree-Structured Parzen Estimator）的贝叶斯优化算法。本方法有效证明了结合基于查询与无锚点模型对于实现企业文档鲁棒版面分割的优越性。