Seek .(US:SKLTY) 3 6 Ke·2025-10-22 23:15Core Insights - DeepSeek has introduced "DeepSeek-OCR," a model that utilizes "Context Optical Compression," significantly enhancing the efficiency of processing textual information from images [1][2][7] - The model demonstrates that images can serve as efficient carriers of information, challenging the traditional reliance on text-based processing [2][6] Group 1: Image Processing Efficiency - DeepSeek-OCR processes documents by treating text as images, compressing entire pages into a few visual tokens, achieving a tenfold efficiency increase with a 97% accuracy rate [1][2] - Traditional methods require thousands of tokens for a lengthy article, while DeepSeek-OCR only needs about 100 visual tokens, allowing it to handle long documents without resource constraints [2][3] Group 2: System Architecture and Functionality - The system consists of two modules: a powerful DeepEncoder that captures page information and a lightweight text generator that converts visual tokens into readable output [3] - The encoder combines local analysis and global understanding, reducing the initial 4096 tokens to just 256, showcasing a 90% reduction compared to competitors [3][4] - In practical tests, a single A100 GPU can process over 200,000 pages daily, with potential scalability to 33 million pages across multiple servers [3][4] Group 3: Information Density and Model Training - The paradox of image data being more efficient lies in its information density; images can encapsulate more data compactly compared to text tokens, which require extensive dimensional expansion [4][5] - While DeepSeek-OCR proves the feasibility of visual tokens, training purely visual models remains a challenge due to the ambiguity in predicting image segments [5][9] Group 4: Potential Impact and Applications - If widely adopted, this technology could transform the "token economy," significantly reducing processing costs for long documents and enhancing data extraction from complex formats [6][7] - It could also improve chatbots' long-term memory by converting old conversations into low-resolution images, simulating human memory decay while extending context without increasing token consumption [6][11] Group 5: Conclusion - The exploration of DeepSeek-OCR not only achieves a tenfold efficiency improvement but also redefines the boundaries of document processing, challenging existing limitations and optimizing cost structures [7][8]