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DeepSeek-OCR

Name: DeepSeek-OCR
Brand: DeepSeek-OCR
Rating: 5 (362 reviews)

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362 Upvotes

About

DeepSeek-OCR is an advanced Optical Character Recognition (OCR) model developed by deepseek-ai, hosted on Hugging Face. It is categorized as an Image-Text-to-Text model, supporting multilingual processing and focusing on 'Contexts Optical Compression'. The model provides detailed usage instructions for inference using Hugging Face transformers on NVIDIA GPUs and is officially supported in vLLM for accelerated inference and PDF processing. It includes visualizations of its capabilities and acknowledges contributions from various related projects and benchmarks. The model has a 3B parameter size and is licensed under MIT.

Categories & Tags

Open Source Artificial Intelligence GitHub #Minimal #Functional #Developer-focused #Clean #Information-dense

Color Palette

Background White

#FFFFFF

70%

Text Black/Dark Grey

#000000

20%

Hugging Face Blue (Links/Logo)

#0070F0

Hugging Face Badge Yellow

#FFC107

Typography

Sans-serif

Headings and Body Text

Design Review

The design of the DeepSeek-OCR model card on Hugging Face is highly functional and adheres to the platform's established aesthetic. It's a light theme with a predominantly white background and dark text, ensuring excellent readability. The use of a clean sans-serif font throughout contributes to a modern and professional feel. Key information is well-structured with clear headings and code blocks, making it easy for developers and researchers to find usage instructions and technical details. The Hugging Face blue is consistently used for links and branding, providing a familiar and navigable experience. Accent colors from badges (like the yellow/orange for the Hugging Face badge and blue for Discord) add small visual cues without cluttering the interface. The inclusion of visual examples effectively demonstrates the model's capabilities. Overall, the design prioritizes clarity, information accessibility, and developer usability over elaborate visual flair, which is appropriate for a technical resource.