DeepSeek-OCR: Contexts Optical Compression
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.
68f848877669bcaeecce0d44
Haoran Wei
admin_assigned
2025-10-24T01:11:17.052Z
6436618aeef1f55654a9f458
https://cdn-avatars.huggingface.co/v1/production/uploads/6436618aeef1f55654a9f458/OvxGtuDg2GAFG9As-2hzW.jpeg
Haoran Wei
user
HaoranWei
68f848877669bcaeecce0d45
Yaofeng Sun
admin_assigned
2025-10-24T01:11:26.247Z
656b0b6cc4d6794d7f367fed
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/vdJ3GU8J-9YfMJ7lI4bif.jpeg
Yaofeng Sun
user
yaofengsun
68f848877669bcaeecce0d46
Yukun Li
DeepSeek-OCR: Contexts Optical Compression
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.
DeepSeek-OCR uses optical 2D mapping to compress long contexts, achieving high OCR precision with reduced vision tokens and demonstrating practical value in document processing.
DeepSeek-OCR DeepEncoder DeepSeek3B-MoE-A570M optical 2D mapping vision tokens OCR precision compression ratio OmniDocBench GOT-OCR2.0 MinerU2.0 LLMs VLMs