HybriDLA: Hybrid Generation for Document Layout Analysis
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912727602036736 |
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| author | Chen, Yufan Moured, Omar Liu, Ruiping Zheng, Junwei Peng, Kunyu Zhang, Jiaming Stiefelhagen, Rainer |
| author_facet | Chen, Yufan Moured, Omar Liu, Ruiping Zheng, Junwei Peng, Kunyu Zhang, Jiaming Stiefelhagen, Rainer |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_19919 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HybriDLA: Hybrid Generation for Document Layout Analysis Chen, Yufan Moured, Omar Liu, Ruiping Zheng, Junwei Peng, Kunyu Zhang, Jiaming Stiefelhagen, Rainer Computer Vision and Pattern Recognition 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. |
| title | HybriDLA: Hybrid Generation for Document Layout Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.19919 |