DeepSeek-OCR is built to handle a wide variety of document types and layouts, going far beyond the simple page scans that traditional OCR systems were designed for. At its core, the model is trained on visually diverse, multi-format data that includes text-heavy pages, tables, diagrams, mathematical equations, and even embedded images. This allows it to interpret complex documents such as research papers, legal contracts, technical manuals, and multilingual reports with high structural fidelity. Unlike character-based OCR tools that often lose layout or misinterpret columns, DeepSeek-OCR understands the spatial relationships between elements on a page, preserving their format during reconstruction. As a result, developers can extract structured outputs like Markdown, HTML, or JSON while keeping the original document’s hierarchy intact.
In terms of layout diversity, DeepSeek-OCR performs particularly well on multi-column text, tables, and forms—common pain points for conventional OCR systems. The model’s visual encoder learns to recognize column boundaries, cell borders, and other layout indicators, allowing it to reproduce tables accurately and maintain column order in multi-layout pages. For example, a financial report with side-by-side charts and text paragraphs can be processed without losing alignment or context. It also supports mathematical and chemical notations, enabling it to interpret formulas and scientific symbols that are often ignored or misread by standard OCR engines. Additionally, DeepSeek-OCR can reconstruct diagrams and flowcharts in descriptive text form, preserving their informational structure even if the image content itself isn’t fully transcribed.
DeepSeek-OCR also supports a broad range of language scripts and document origins, including Latin, Cyrillic, and East Asian languages such as Chinese, Japanese, and Korean. This makes it suitable for global datasets and multilingual archives. While extremely low-resolution scans or handwritten notes can still challenge the system, the model compensates by using optical compression to retain overall layout and text flow, even when pixel quality is poor. In short, DeepSeek-OCR excels in processing structured, multi-format, and multilingual documents—handling everything from dense academic PDFs to complex enterprise forms—while preserving both the accuracy of the text and the integrity of the original design.
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