HybriDLA: Hybrid Generation for Document Layout Analysis

• URL: http://arxiv.org/abs/2511.19919v1
• Date: Tue, 25 Nov 2025 04:53:47 GMT
• Title: HybriDLA: Hybrid Generation for Document Layout Analysis
• Authors: Yufan Chen, Omar Moured, Ruiping Liu, Junwei Zheng, Kunyu Peng, Jiaming Zhang, Rainer Stiefelhagen,
• Abstract summary: HybriDLA is a novel generative framework that unifies diffusion and autoregressive decoding within a single layer.<n>This architecture elevates performance to 83.5% mean Average Precision (mAP)
• Score: 40.47982474843359
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional document layout analysis (DLA) traditionally depends on empirical priors or a fixed set of learnable queries executed in a single forward pass. While sufficient for early-generation documents with a small, predetermined number of regions, this paradigm struggles with contemporary documents, which exhibit diverse element counts and increasingly complex layouts. To address challenges posed by modern documents, we present HybriDLA, a novel generative framework that unifies diffusion and autoregressive decoding within a single layer. The diffusion component iteratively refines bounding-box hypotheses, whereas the autoregressive component injects semantic and contextual awareness, enabling precise region prediction even in highly varied layouts. To further enhance detection quality, we design a multi-scale feature-fusion encoder that captures both fine-grained and high-level visual cues. This architecture elevates performance to 83.5% mean Average Precision (mAP). Extensive experiments on the DocLayNet and M$^6$Doc benchmarks demonstrate that HybriDLA sets a state-of-the-art performance, outperforming previous approaches. All data and models will be made publicly available at https://yufanchen96.github.io/projects/HybriDLA.

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