DeepSeek-OCR: A New Milestone in Information Processing

In an era of rapid AI advancements, it has been discovered that images can efficiently carry vast amounts of text information. This week, DeepSeek open-sourced a model named 'DeepSeek-OCR', introducing the concept of 'Context Optical Compression'.

While market discussions are still limited, this development could mark a quiet yet significant turning point in AI evolution. DeepSeek-OCR processes text as images, compressing entire page content into a few 'visual tokens' and then decoding them back into text, tables, or charts.

The result is a tenfold increase in efficiency with an accuracy of 97%. This is not merely a technical optimization but an attempt to prove that images are efficient carriers of information. For instance, a thousand-word article traditionally requires over a thousand tokens, while DeepSeek needs only about 100 visual tokens to restore it with 97% fidelity.

The system design of DeepSeek-OCR resembles a precision machine, consisting of a powerful DeepEncoder and a lightweight text generator. The encoder combines the local analysis ability of SAM with the global understanding of CLIP, compressing the initial 4096 tokens to just 256, which is the core secret of its efficiency.

If this technology matures and becomes widespread, it will transform the 'token economy', enhance information extraction, and improve flexibility. It can also enhance the long-conversation memory of chatbots. DeepSeek-OCR's exploration redefines document processing boundaries, optimizes cost structures, and innovates enterprise processes.