PromptDLA: A Domain-aware Prompt Document Layout Analysis Framework with Descriptive Knowledge as a Cue

2026-03-10

AI Summary

This work addresses the limited generalization of existing document layout analysis methods in cross-domain settings, which stems from their neglect of inherent structural differences among datasets. To overcome this, we propose a domain-aware prompting mechanism that leverages descriptive knowledge as domain-specific prior cues to dynamically generate tailored prompts, guiding the model to focus on salient layout features. By integrating prompt learning with a layout analysis architecture, our approach enables end-to-end domain-adaptive training. Extensive experiments on multiple benchmark datasets—including DocLayNet, PubLayNet, M6Doc, and D⁴LA—demonstrate that our method significantly outperforms current state-of-the-art approaches, achieving superior performance across diverse domains.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Natural Language Processing: Prompt Engineering / Prompting
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Document Layout Analysis (DLA) is crucial for document artificial intelligence and has recently received increasing attention, resulting in an influx of large-scale public DLA datasets. Existing work often combines data from various domains in recent public DLA datasets to improve the generalization of DLA. However, directly merging these datasets for training often results in suboptimal model performance, as it overlooks the different layout structures inherent to various domains. These variations include different labeling styles, document types, and languages. This paper introduces PromptDLA, a domain-aware Prompter for Document Layout Analysis that effectively leverages descriptive knowledge as cues to integrate domain priors into DLA. The innovative PromptDLA features a unique domain-aware prompter that customizes prompts based on the specific attributes of the data domain. These prompts then serve as cues that direct the DLA toward critical features and structures within the data, enhancing the model's ability to generalize across varied domains. Extensive experiments show that our proposal achieves state-of-the-art performance among DocLayNet, PubLayNet, M6Doc, and D$^4$LA. Our code is available at https://github.com/Zirui00/PromptDLA.

Problem

• Document Layout Analysis
• domain-aware
• layout structure
• dataset merging
• generalization

Innovation

• domain-aware prompting
• document layout analysis
• descriptive knowledge
• prompt engineering
• cross-domain generalization