Information extraction from documents has become increasingly popular due to the rise of large language models (LLMs) and re-augmented generation (RAG) models. Document Layout Analysis (DLA) is a fundamental task in document AI, playing a crucial role in identifying semantically related elements within a document—a key step toward effective information extraction. Modern document layout analysis algorithms benefit from large-scale annotated datasets but suffer significant performance drops when tested across different datasets, limiting the generalization of models trained on a single source. To address this, we utilize a multi-dataset training approach for a Universal Layout Detection (UniLayDet) model utilizing a shared detection architecture with dataset-specific outputs and unifying the label space post-training through an automatic merging process. UniLayDet significantly improves generalization across datasets compared to models trained individually and also achieves competitive in-domain performance, notably attaining a mAP (@IoU[0.5–0.95]) of 68.9% on M \(^6\) Doc (partitioned setting), close to the existing SOTA of 69.9%, showing that our model is simple yet effective for the task. Code and models are available at github.com/Mobius1D/UniLayDet .