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

TL;DR

This paper introduces $M^{6}$Doc, a comprehensive large-scale dataset for document layout analysis covering diverse formats, types, layouts, languages, and annotations, along with a transformer-based analysis method called TransDLANet that achieves state-of-the-art results.

Contribution

The paper presents a novel, extensive dataset for modern document layout analysis and a new transformer-based model that improves analysis accuracy across diverse document types.

Findings

$M^{6}$Doc contains 237,116 annotations across 9,080 pages.

TransDLANet achieves 64.5% mAP on $M^{6}$Doc.

The dataset enhances model generalization to real-world documents.

Abstract

Document layout analysis is a crucial prerequisite for document understanding, including document retrieval and conversion. Most public datasets currently contain only PDF documents and lack realistic documents. Models trained on these datasets may not generalize well to real-world scenarios. Therefore, this paper introduces a large and diverse document layout analysis dataset called $M^{6}Doc$. The $M^6$ designation represents six properties: (1) Multi-Format (including scanned, photographed, and PDF documents); (2) Multi-Type (such as scientific articles, textbooks, books, test papers, 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 annotation labels with 237,116 annotation instances in 9,080 manually annotated pages); and (6) Modern documents. Additionally, we propose a transformer-based document layout analysis method called TransDLANet, which leverages an adaptive element matching mechanism that enables query embedding to better match ground truth to improve recall, and constructs a segmentation branch for more precise document image instance segmentation. We conduct a comprehensive evaluation of $M^{6}Doc$ with various layout analysis methods and demonstrate its effectiveness. TransDLANet achieves state-of-the-art performance on $M^{6}Doc$ with 64.5% mAP. The $M^{6}Doc$ dataset will be available at https://github.com/HCIILAB/M6Doc.