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Latest articles and insights about OCR technology
Karpathy Speaks: Did We Feed AI the Wrong "Diet" from the Start?
AI legend Andrej Karpathy dropped a bombshell comment on the DeepSeek-OCR paper: what truly matters isn't OCR performance, but the disruptive idea it reveals—maybe LLM inputs should have always been "pixels" instead of "text." This perspective sparked intense debate in the AI community.
DeepSeek-OCR: Beyond OCR, Towards a New Paradigm of Contextual Compression
While AI models keep getting bigger and more boring, DeepSeek-OCR changes the game. With its "optical context compression" approach, it transforms text into images, enabling AI to grasp content at a glance—just like humans do—rather than processing word by word.
AI's JPEG Moment: Why Silicon Valley Can't Stop Raving About DeepSeek-OCR
DeepSeek's latest open-source model has Silicon Valley buzzing—3B parameters, exponential efficiency gains, elegant simplicity, and what some believe is Google Gemini's closely-guarded trade secret, now open-sourced. Andrej Karpathy weighs in: images are better LLM inputs than text.
DeepSeek-OCR: The Visual Token Compression Breakthrough
DeepSeek's OCR model isn't just another text recognition tool—it's an efficiency revolution for multimodal AI. Using Context-Aware Optical Compression, it outperforms GOT-OCR2.0's 256 tokens with just 100 visual tokens, achieving 97% accuracy at 10x compression.
Why One Visual Token Beats Ten Text Tokens: Information Theory Lessons from DeepSeek-OCR
Is text really the most efficient way to compress information? DeepSeek-OCR answers with hard data. Through its innovative DeepEncoder architecture, this 380M-parameter encoder achieves 10x compression of visual tokens over text tokens while maintaining 97% accuracy.