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articles/ai-services/document-intelligence/includes/input-requirements.md

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Supported file formats:
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|Model | PDF |Image: </br>`JPEG/JPG`, `PNG`, `BMP`, `TIFF`, `HEIF` | Microsoft Office: </br> Word (`DOCX`), Excel (`XLSX`), PowerPoint (`PPTX`), HTML|
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|--------|:----:|:-----:|:---------------:|
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|Read | ✔ | ✔ | ✔ |
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|Layout | ✔ | ✔ | ✔ |
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|General&nbsp;Document| ✔ | ✔ | |
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|Prebuilt | ✔ | ✔ | |
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|Custom extraction | ✔ | ✔ | |
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|Custom classification | ✔ | ✔ | ✔ |
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|Model | PDF |Image: </br>`JPEG/JPG`, `PNG`, `BMP`, `TIFF`, `HEIF` | Microsoft Office: </br> Word (`DOCX`), Excel (`XLSX`), PowerPoint (`PPTX`), HTML|
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|--------|:----:|:-----:|:---------------:|
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|Read ||||
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|Layout ||||
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|General&nbsp;Document||| |
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|Prebuilt ||| |
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|Custom extraction ||| |
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|Custom classification ||||
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* For best results, provide one clear photo or high-quality scan per document.
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articles/ai-services/document-intelligence/prebuilt/layout.md

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#### Output table response to markdown format
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#### Output response to markdown format
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The Layout API can output the extracted text in markdown format. Use the `outputContentFormat=markdown` to specify the output format in markdown. The markdown content is output as part of the `content` section.
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### Sections
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Hierarchical document structure analysis is pivotal in organizing, comprehending, and processing extensive documents. This approach is vital for semantically segmenting long documents to boost comprehension, facilitate navigation, and improve information retrieval. The advent of [Retrieval Augmented Generation (RAG)](../concept/retrieval-augmented-generation.md) in document generative AI underscores the significance of hierarchical document structure analysis. The Layout model supports sections and subsections in the output, which identifies the relationship of sections and object within each section. The hierarchical structure is maintained in `elements` of each section. You can use [output to markdown format](#output-to-markdown-format) to easily get the sections and subsections in markdown.
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Hierarchical document structure analysis is pivotal in organizing, comprehending, and processing extensive documents. This approach is vital for semantically segmenting long documents to boost comprehension, facilitate navigation, and improve information retrieval. The advent of [Retrieval Augmented Generation (RAG)](../concept/retrieval-augmented-generation.md) in document generative AI underscores the significance of hierarchical document structure analysis. The Layout model supports sections and subsections in the output, which identifies the relationship of sections and object within each section. The hierarchical structure is maintained in `elements` of each section. You can use [output response to markdown format](#output-response-to-markdown-format) to easily get the sections and subsections in markdown.
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#### [Sample code](#tab/sample-code)
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