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Azure Content Understanding: analyzers and structured extraction

How analyzers turn documents, images, audio and video into structured JSON, and how to call them from code.

From Ultra Transcenders AI-901 by Tony Rough (publishing soon)

Content Understanding combines OCR and layout analysis with generative models so that you describe the fields you want and get them back as structured output. It works across documents, images, audio and video. Figure 9.1 shows the path from content to structured result.

Documents, images, audio or video go into an analyzer, either prebuilt or custom with your own field schema. The analyzer first extracts the content, then applies the field schema, and returns structured JSON with markdown content and the fields. Below, the asynchronous call is shown: begin_analyze() returns a poller at once, and poller.result() polls until the job completes and returns the analysis result.
Figure 9.1: How Content Understanding turns content into structured JSON, and how code calls it

Setting it up

What it returns

Analyzers

An analyzer is the configuration that processes one kind of content.

Analyzer Content Notes
Document (prebuilt-document, prebuilt-invoice, prebuilt-read, prebuilt-layout) PDFs (including scanned), Office files, forms OCR and layout, then fields
Image Still images Not PDFs
Audio (prebuilt-audio, prebuilt-audioSearch, prebuilt-callCenter) Voicemails, calls Transcription to WebVTT with diarization, speaker roles, optional word timestamps, then fields
Video Video files Keyframes and scenes

Defining fields

Calling it from code

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