Abstract
Document layout analysis aims to detect and categorize structural elements (e.g., titles, tables, figures) in scanned or digital documents. Popular methods often rely on high-quality Optical Character Recognition (OCR) to merge visual features with extracted text. This dependency introduces two major drawbacks: propagation of text recognition errors and substantial computational overhead, limiting the robustness and practical applicability of multimodal approaches. In contrast to the prevailing multimodal trend, we argue that effective layout analysis depends not on text-visual fusion, but on a deep understanding of documents' intrinsic visual structure. To this end, we propose PARL (Position-Aware Relation Learning Network), a novel OCR-free, vision-only framework that models layout through positional sensitivity and relational structure. Specifically, we first introduce a Bidirectional Spatial Position-Guided Deformable Attention module to embed explicit positional dependencies among layout elements directly into visual features. Second, we design a Graph Refinement Classifier (GRC) to refine predictions by modeling contextual relationships through a dynamically constructed layout graph. Extensive experiments show PARL achieves state-of-the-art results. It establishes a new benchmark for vision-only methods on DocLayNet and, notably, surpasses even strong multimodal models on M6Doc. Crucially, PARL (65M) is highly efficient, using roughly four times fewer parameters than large multimodal models (256M), demonstrating that sophisticated visual structure modeling can be both more efficient and robust than multimodal fusion.
Abstract (translated)
文档布局分析的目标是检测和分类扫描或数字文档中的结构元素（例如，标题、表格、图片等）。现有流行的方法通常依赖于高质量的光学字符识别（OCR）技术，将视觉特征与提取出来的文本结合起来。这种依赖性带来了两个主要缺点：文字识别错误的传播以及计算开销过大，这限制了多模态方法的鲁棒性和实用性。 不同于当前流行的多模态趋势，我们认为有效的布局分析并不需要文字和图像的信息融合，而是基于对文档内在视觉结构的深刻理解。为此，我们提出了PARL（位置感知关系学习网络），这是一种新颖的、不依赖OCR的纯视觉框架，通过引入位置敏感性和关联性来建模布局。 具体来说，我们首先提出了一种双向空间位置引导的变形注意力模块，将布局元素之间的显式位置依存关系直接嵌入到视觉特征中。其次，设计了一个图细化分类器（GRC），该分类器通过动态构建的布局图模型上下文关系来优化预测结果。 大量的实验表明PARL实现了最先进的效果，在DocLayNet数据集上为纯视觉方法建立了新的基准，并且在M6Doc数据集中甚至超过了强大的多模态模型。特别重要的是，PARL（具有约6500万个参数）非常高效，使用的参数仅为大规模的多模态模型（大约2.5亿个参数）的四分之一左右。这表明复杂视觉结构建模可以比多模态融合更为有效且鲁棒。
URL
https://arxiv.org/abs/2601.07620
