We explore DeepSeek AI's groundbreaking idea of turning long documents into dense visual tokens to bypass transformer context limits. DeepSeek OCR uses a two-path encoder (an 80M SAM-based local reader and a 300M CLIP-based global model) connected by a 16x convolutional compressor, feeding a 570M-parameter MOE decoder. With 10x–20x compression, it achieves high OCR accuracy on Fox benchmarks, outperforms rivals with far fewer tokens, and scales to industrial volumes (200k pages/day on a single A100). We discuss implications for memory and potentially unlimited-context architectures, and note that the project is open-sourced for researchers and educators alike.

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