OXR
An open-source document parsing model developed by the SoMarkAI team with 1.3B parameters. It converts PDFs and document images into Markdown or JSON with layout coordinates, recognizes 13 element types, and employs pluggable layout analysis with a recursive self-improvement post-training loop.
Timeline (last 90 days)
SoMarkAI团队开源了1.3B参数的文档解析模型OXR。
OXR将布局分析拆分为独立模型，允许用户替换为自定义布局模型。
OXR在本地运行需要24 GB NVIDIA GPU。
OXR团队提供了免费托管API供测试自有文件，但共享队列不适合生产环境。
多栏布局是OXR的已知弱项，而多栏恰是学术论文最常见的版式。
OXR的后训练阶段采用错误样本驱动的迭代数据增强：模型犯错或不确定的样本被转化为文字理由，理由聚类后驱动下一轮定向训练数据生成。
Running OXR locally requires Linux and an NVIDIA GPU with BF16 support; 24GB of VRAM is enough for low-concurrency testing, while long documents or high concurrency call for 48GB or more; the Docker all-in-one image is the easiest way to get started.
OXR is an open-weight document parsing model with only 1.3B parameters, developed by the SoMarkAI team, aiming to convert PDFs and document images directly into Markdown or JSON with layout coordinates.
OXR's repository is at GitHub's SoMarkAI/OXR, the weights are at Hugging Face's SoMarkAI/OXR-1.0, and it is released simultaneously on ModelScope; the license is Apache 2.0 with additional terms, including threshold restrictions on commercial use.
OXR offers a free API that requires no registration or API key, but it is for testing only and requests are queued; the team said it will stay online until it can no longer afford the cost.
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All Facts (15)
OXR的后训练阶段采用错误样本驱动的迭代数据增强：模型犯错或不确定的样本被转化为文字理由，理由聚类后驱动下一轮定向训练数据生成。
50%UnverifiedOXR将布局分析拆分为独立模型，允许用户替换为自定义布局模型。
50%UnverifiedOXR在本地运行需要24 GB NVIDIA GPU。
50%UnverifiedOXR团队提供了免费托管API供测试自有文件，但共享队列不适合生产环境。
50%Unverified多栏布局是OXR的已知弱项，而多栏恰是学术论文最常见的版式。
50%UnverifiedOXR decouples layout analysis from recognition: recognition and reading order are executed in parallel within the same model to ensure speed, while layout analysis is an independent model that can be replaced as needed.
50%UnverifiedRunning OXR locally requires Linux and an NVIDIA GPU with BF16 support; 24GB of VRAM is enough for low-concurrency testing, while long documents or high concurrency call for 48GB or more; the Docker all-in-one image is the easiest way to get started.
50%UnverifiedOXR is an open-weight document parsing model with only 1.3B parameters, developed by the SoMarkAI team, aiming to convert PDFs and document images directly into Markdown or JSON with layout coordinates.
50%UnverifiedOXR's repository is at GitHub's SoMarkAI/OXR, the weights are at Hugging Face's SoMarkAI/OXR-1.0, and it is released simultaneously on ModelScope; the license is Apache 2.0 with additional terms, including threshold restrictions on commercial use.
50%UnverifiedOXR offers a free API that requires no registration or API key, but it is for testing only and requests are queued; the team said it will stay online until it can no longer afford the cost.
50%UnverifiedOXR officially claims to rank first simultaneously on three mainstream document parsing benchmarks: olmOCR-Bench, OmniDocBench v1.6, and DocParsingBench.
50%UnverifiedIn a test running all 21 models on a fixed set of 100 OmniDocBench pages on a single RTX PRO 6000, OXR end-to-end (including layout analysis, recognition, and reading order) processed 100 pages in 101 seconds, while the next fastest, OvisOCR2, took 135 seconds.
50%UnverifiedOXR supports recognizing 13 element types and outputting bounding boxes, including tables, formulas, chemical equations, chemical structure diagrams, and code blocks.
50%UnverifiedOXR's engineering design includes an RSI post-training loop, which uses recursive self-improvement to automatically convert parsing errors and uncertain results into targeted training data without manual annotation.
50%UnverifiedSoMarkAI团队开源了1.3B参数的文档解析模型OXR。
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