PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B
  Ultra-Compact Vision-Language Model
In this report, we propose PaddleOCR-VL, a SOTA and resource-efficient model
tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a
compact yet powerful vision-language model (VLM) that integrates a NaViT-style
dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to
enable accurate element recognition. This innovative model efficiently supports
109 languages and excels in recognizing complex elements (e.g., text, tables,
formulas, and charts), while maintaining minimal resource consumption. Through
comprehensive evaluations on widely used public benchmarks and in-house
benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document
parsing and element-level recognition. It significantly outperforms existing
solutions, exhibits strong competitiveness against top-tier VLMs, and delivers
fast inference speeds. These strengths make it highly suitable for practical
deployment in real-world scenarios.
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2025-10-18T04:49:31.953Z
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2025-10-21T14:06:01.510Z
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Yubo Zhang
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2025-10-21T14:05:58.937Z
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Yanjun Ma
PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B
  Ultra-Compact Vision-Language Model
In this report, we propose PaddleOCR-VL, a SOTA and resource-efficient model
tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a
compact yet powerful vision-language model (VLM) that integrates a NaViT-style
dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to
enable accurate element recognition. This innovative model efficiently supports
109 languages and excels in recognizing complex elements (e.g., text, tables,
formulas, and charts), while maintaining minimal resource consumption. Through
comprehensive evaluations on widely used public benchmarks and in-house
benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document
parsing and element-level recognition. It significantly outperforms existing
solutions, exhibits strong competitiveness against top-tier VLMs, and delivers
fast inference speeds. These strengths make it highly suitable for practical
deployment in real-world scenarios.
PaddleOCR-VL, a vision-language model combining NaViT-style visual encoder and ERNIE-4.5 language model, achieves state-of-the-art performance in document parsing with minimal resource consumption.
vision-language model NaViT-style dynamic resolution visual encoder ERNIE-4.5 element recognition page-level document parsing element-level recognition inference speeds