dots.ocr SOTA Document Parsing in a Compact VLM

dots.ocr is a powerful, multilingual document parsing model from rednote-hilab that achieves state-of-the-art performance by unifying layout detection and content recognition within a single, efficient vision-language model (VLM). Built upon a compact 1.7B parameter Large Language Model (LLM), it offers a streamlined alternative to complex, multi-model pipelines, enabling faster inference speeds. The model demonstrates superior capabilities across multiple industry benchmarks, including OmniDocBench, where it leads in text, table, and reading order tasks, and olmOCR-bench, where it achieves the highest overall score. Its key strengths include robust parsing of low-resource languages, task flexibility through simple prompt alteration, and the ability to generate structured output in JSON and Markdown formats. While the model has limitations in handling highly complex tables, formulas, and picture content, future development is focused on enhancing these areas and creating a more general-purpose perception model. Resources:  dots.ocr github repo: https://github.com/rednote-hilab/dots.ocr Start a career in AI: https://opencv.org/university Get help building your computer vision and AI solutions : http://bigvision.ai

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