DeepSeek-OCR: Contexts Optical Compression
Authors: name: Haoran Wei; name: Yaofeng Sun; name: Yukun Li
Published: 2025-10-21 02:41:44

Abstract: We present DeepSeek-OCR as an initial investigation into the feasibility of
compressing long contexts via optical 2D mapping. DeepSeek-OCR consists of two
components: DeepEncoder and DeepSeek3B-MoE-A570M as the decoder. Specifically,
DeepEncoder serves as the core engine, designed to maintain low activations
under high-resolution input while achieving high compression ratios to ensure
an optimal and manageable number of vision tokens. Experiments show that when
the number of text tokens is within 10 times that of vision tokens (i.e., a
compression ratio < 10x), the model can achieve decoding (OCR) precision of
97%. Even at a compression ratio of 20x, the OCR accuracy still remains at
about 60%. This shows considerable promise for research areas such as
historical long-context compression and memory forgetting mechanisms in LLMs.
Beyond this, DeepSeek-OCR also demonstrates high practical value. On
OmniDocBench, it surpasses GOT-OCR2.0 (256 tokens/page) using only 100 vision
tokens, and outperforms MinerU2.0 (6000+ tokens per page on average) while
utilizing fewer than 800 vision tokens. In production, DeepSeek-OCR can
generate training data for LLMs/VLMs at a scale of 200k+ pages per day (a
single A100-40G). Codes and model weights are publicly accessible at
http://github.com/deepseek-ai/DeepSeek-OCR.