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dots.ocr SOTA Document Parsing in a Compact VLM
Manage episode 516275975 series 3693358
Dr. Satya Mallick에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Dr. Satya Mallick 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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
6 에피소드
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