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Document layout analysis is a crucial prerequisite for documentunderstanding, including document retrieval and conversion. Most publicdatasets currently contain only PDF documents and lack realistic documents.Models trained on these datasets may not generalize well to real-worldscenarios. Therefore, this paper introduces a large and diverse document layoutanalysis dataset called $M^{6}Doc$. The $M^6$ designation represents sixproperties: (1) Multi-Format (including scanned, photographed, and PDFdocuments); (2) Multi-Type (such as scientific articles, textbooks, books, testpapers, magazines, newspapers, and notes); (3) Multi-Layout (rectangular,Manhattan, non-Manhattan, and multi-column Manhattan); (4) Multi-Language(Chinese and English); (5) Multi-Annotation Category (74 types of annotationlabels with 237,116 annotation instances in 9,080 manually annotated pages);and (6) Modern documents. Additionally, we propose a transformer-based documentlayout analysis method called TransDLANet, which leverages an adaptive elementmatching mechanism that enables query embedding to better match ground truth toimprove recall, and constructs a segmentation branch for more precise documentimage instance segmentation. We conduct a comprehensive evaluation of$M^{6}Doc$ with various layout analysis methods and demonstrate itseffectiveness. TransDLANet achieves state-of-the-art performance on $M^{6}Doc$with 64.5% mAP. The $M^{6}Doc$ dataset will be available athttps://github.com/HCIILAB/M6Doc.

M$^{6}$Doc: A Large-Scale Multi-Format, Multi-Type, Multi-Layout,  Multi-Language, Multi-Annotation Category Dataset for Modern Document Layout  Analysis