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{"id":6506,"date":"2025-10-24T01:37:10","date_gmt":"2025-10-24T01:37:10","guid":{"rendered":"https:\/\/www.myshirtai.com\/archives\/6506"},"modified":"2025-10-24T01:37:10","modified_gmt":"2025-10-24T01:37:10","slug":"deepseek-ocr%ef%bc%9a%e5%bc%80%e5%90%af%e8%a7%86%e8%a7%89%e5%8e%8b%e7%bc%a9%e6%96%b0%e7%ba%aa%e5%85%83","status":"publish","type":"post","link":"https:\/\/www.myshirtai.com\/en\/archives\/6506","title":{"rendered":"DeepSeek-OCR: Opening a New Era of Visual Compression"},"content":{"rendered":"

OCR Evolution and Challenges<\/h2>\n\n\n\n

OCR (Optical Character Recognition) technology has a history of many years, it is from the initial scanning text extraction to today's intelligent recognition, has brought great convenience to our daily work. However, as the needs of text processing become more and more complex, OCR is also facing a new challenge: how to deal with large amounts of long text and complex document structure?<\/p>\n\n\n\n

Deep Learning Improves OCR Accuracy and Efficiency<\/h3>\n\n\n\n

Traditional OCR is able to recognize printed text, but is often overwhelmed when faced with complex layouts and mixed-text documents.The DeepSeek team introduced the DeepSeek-OCR<\/strong>Based on the Visual Language Model (VLM) and the new \"Contextual Optical Compression\" technology, it breaks through the limitations of traditional OCR and provides a new way of thinking for the evolution of OCR technology.<\/p>\n\n\n\n

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Visual Compression and Contextualization<\/h2>\n\n\n\n

The core innovation of DeepSeek-OCR is the introduction of the Visual-Text Compression<\/strong> The new approach. It achieves efficient long text processing by converting images into visual tokens and significantly reducing the number of tokens required for text through compression techniques.<\/p>\n\n\n\n

Visual compression: a small number of tokens for efficient text processing<\/h3>\n\n\n\n

While traditional text processing relies on one-dimensional tokens (words or bytes) for computation, DeepSeek-OCR drastically reduces the number of tokens required for computation by transforming document images into two-dimensional visual tokens. Unlike traditional OCR models that require thousands of tokens to decode a document, DeepSeek-OCR can outperform traditional OCR models with only a small number of visual tokens (e.g., 100).<\/p>\n\n\n\n

This approach not only improves the compression efficiency, but also greatly reduces the computational resource consumption of AI models when processing long texts.<\/p>\n\n\n\n

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Contextual Optical Compression: Compressing the \"Memory\" Problem in Long Texts<\/h3>\n\n\n\n

When processing long texts, AI models usually face a bottleneck of contextual memory.DeepSeek-OCR proposes a Contextual Optical Compression<\/strong>(Contextual Optical Compression) method, which can compress lengthy contextual information into fewer visual tokens by means of images, thus realizing efficient storage and retrieval of memories.<\/p>\n\n\n\n

In this way, DeepSeek-OCR is able to dramatically reduce the number of tokens without losing information accuracy, making Large Language Models (LLMs) more efficient in processing long texts. This innovation opens up new paths for future applications of AI in areas such as long text processing, context understanding, and memory optimization.<\/p>\n\n\n\n

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The Power of DeepSeek-OCR<\/h2>\n\n\n\n

Balancing Compression Rate and Accuracy<\/h3>\n\n\n\n

According to the experimental data, DeepSeek-OCR is able to maintain a compression ratio of up to 10x with a high 97%<\/strong> OCR accuracy; even at compression ratios of 20 times<\/strong> The accuracy is still maintained in the case of 60%<\/strong> Around.<\/p>\n\n\n\n

compression ratio<\/strong><\/th>OCR Accuracy<\/strong><\/th>application scenario<\/strong><\/th><\/tr><\/thead>
10 times<\/strong><\/td>97%<\/strong><\/td>Efficient Document Processing<\/td><\/tr>
20 times<\/strong><\/td>60%<\/strong><\/td>Long Text and Complex Documents<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

These results show that DeepSeek-OCR not only provides superior compression capabilities in theory, but its performance in real-world applications is also excellent.<\/p>\n\n\n\n

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Leading benchmark scores<\/h3>\n\n\n\n

exist OmniDocBench<\/strong> In the benchmarking test, DeepSeek-OCR was used to 100 visual tokens<\/strong> Beyond the use of 256 tokens<\/strong> (used form a nominal expression) GOT-OCR2.0<\/strong>and is used in less than 800 visual tokens<\/strong> The situation is beyond the MinerU2.0<\/strong>The latter requires about 7000 tokens<\/strong>. These results show the superiority and efficiency of DeepSeek-OCR in real OCR tasks.<\/p>\n\n\n\n

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Examples of practical applications<\/h2>\n\n\n\n

Financial Research Paper Document Analysis<\/h3>\n\n\n\n

Suppose we have a typical Financial studies<\/strong>In the traditional OCR model, text is usually extracted as a normal txt file, but information such as tables and charts cannot be accurately retained or reproduced. In traditional OCR models, the scanned text is usually extracted as a normal txt file, but information such as tables and charts cannot be accurately preserved or reproduced. DeepSeek-OCR performs particularly well for such documents.<\/p>\n\n\n\n