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To turn a scanned PDF into structured data, first run OCR to recognize the text, then use a document-analysis model or processor that identifies the structures you need—such as table cells, form fields, or selection marks. Validate and normalize those results into your target schema. A searchable PDF is a different output: it adds a text layer to the scanned pages but does not, by itself, create database-ready records.
1. Check the PDF and decide what you need back
A scanned PDF may contain page images rather than usable text. If it already has a text layer, ordinary text extraction may be enough; if not, OCR is needed to recognize its contents. Check that pages are legible and correctly oriented before processing. OCR services differ in their image handling, and poor scans may still produce errors.
Choose the deliverable before selecting a tool:
- Searchable PDF: recognized text is embedded alongside the scanned page image so the document can be searched or copied.
- Plain text: recognized words without the relationships needed to represent tables or forms.
- Layout-aware output: text linked to page positions and structural elements, often returned as JSON.
- Structured fields or records: values such as invoice numbers, form entries, or table rows mapped into a schema for downstream use.
These outputs are not interchangeable. For example, Microsoft’s prebuilt Read model documents searchable-PDF output, while its Layout model returns structural elements. A searchable PDF alone does not produce normalized rows for a database.
2. Choose an OCR model or processor for the structures you need
OCR recognizes text; structured extraction also identifies relationships, such as which words belong in a table cell or which value corresponds to a form label. Feature availability varies by model and service, so match the processor to the desired output rather than choosing on the basis of the word “OCR” alone.
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| Service | Documented output and capabilities | What to verify |
|---|---|---|
| Azure AI Document Intelligence | The Layout model documents text, tables, selection marks, and sections. The Read model supports searchable PDF output under its documented API version. See Microsoft’s Layout, Read, and response documentation. | Confirm the model, API version, supported input, and current account limits. The cited Layout input guide lists a file-size limit below 50 MB, up to 2,000 PDF/TIFF pages processed, and only the first two pages processed on the free tier. These are Azure-specific limits, not universal OCR limits. |
| Google Cloud Document AI | The Document response includes recognized text and page-level OCR/layout objects, including text anchors. The service overview describes processors for OCR, layout, key-value pairs, tables, classification, and splitting. | Select the processor for the document task and check its current input, online-processing, and batch constraints. |
| AWS Textract-backed workflow | The cited AWS Comprehend guide says Comprehend uses Textract for image files and scanned PDFs by default, and describes using AnalyzeDocument with TABLES and FORMS features for additional information. | Confirm the integration and output features required for your workflow. The cited guide establishes this integration path, not a feature-by-feature comparison with Azure or Google. |
The reviewed documentation does not establish a neutral accuracy winner or a comparable price ranking. Compare the output you need, traceability to page coordinates, input and page limits, batch workflow, deployment and integration requirements, privacy and retention constraints, expected-volume cost, and the amount of human review required.
3. Test a representative sample and inspect the raw output
Before processing a large collection, run documents that reflect the variation in your real PDFs: different layouts, scan quality, page orientations, and document types. Inspect the returned text and structures, not just whether the job completed.
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Where the service provides them, retain page numbers, text spans or anchors, coordinates, and confidence values. These references help connect an extracted field or table cell back to its source region. Google documents text anchors and page-level layout objects in its Document response; Microsoft describes word and table-cell geometry in its Layout output.
A confidence score is a signal to review, not proof that a value is correct. Do not treat a sample score as a service-wide accuracy result or rely on it alone for high-impact fields.
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4. Normalize the extracted values and validate them
OCR output is not automatically a clean record set. Define a stable target schema, map each recognized value into it, and keep source references so a reviewer can find the original page or region.
- Check required fields and expected formats, such as dates, identifiers, and amounts.
- Review uncertain values and fields where an error would have significant consequences.
- Check table boundaries and relationships, not only whether individual words were recognized.
- Preserve page and region references through transformation so corrections remain traceable.
Do not assume that recognized text has been normalized or that a table or form has been interpreted correctly simply because the output is structured.
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5. Confirm processing constraints before scaling up
Check the chosen service’s current documentation and account configuration before sending a full batch. Confirm accepted file formats and sizes, page limits, page selection, synchronous or batch options, API version, and region. Also review privacy, retention, and deployment requirements for the documents you will process.
Limits depend on the service, processor, API version, and plan. For example, Microsoft’s cited Layout input guide lists Azure-specific PDF/TIFF limits; those figures should not be applied to Google Document AI, AWS Textract, or another Azure model without checking its own current documentation.
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