Use case
OCR
Turning images of text — scans, photographs, faxes, historic files — into machine-readable text with layout. The step everything else depends on: no extraction, search or classification pipeline is better than the text layer underneath it.
- Source
- Editorial ontology entry — no fetched document behind this page
- Evidence
- none on this page — it links to the pages that hold it
- Category
- Documents
- Typical risk
- medium
- Entry
- editorial · reviewed 25 Aug 2026
01What this is
OCR covers printed text, handwriting, tables and mixed scripts. Modern pipelines combine layout detection with recognition and increasingly with vision-language models, and emit structured output (reading order, tables, headings) rather than a flat string.
A good deployment measures character and field accuracy on a sample of your own documents, keeps the page image beside the text for verification, handles the languages actually present — Traditional and Simplified Chinese, Japanese, Korean scripts have distinct failure modes — and flags low-confidence regions instead of guessing.
Pitfalls: a PDF that already contains a bad text layer, so nobody runs OCR at all; tables flattened into unusable prose; and rotated or skewed scans processed without correction. Where documents contain identity or health data, the images themselves are the sensitive asset and their storage location matters as much as the text.
- Typical data
- scanned documents, identity documents, forms, handwritten notes
- Solution classes it admits
- Enterprise SaaS, Private cloud, Self-hosted
02Deployment options
- ASSESSMENT
On a neutral reading of this use case, Self-hosted is a strong alternative, Private cloud is a strong alternative and Enterprise SaaS is conditional.
- ASSESSMENT
Read as a generic reading of this page, not a recommendation: no organisation, size, jurisdiction, budget or technical capability has been supplied, so wherever an option depends on one of those, it says "unknown". Ask your own question to get a verdict that accounts for them.
Private cloud
STRONG ALTERNATIVEManaged model API or private model deployment in your cloud account
- ASSESSMENT
Two patterns fit inside one account in a region you name: call a managed foundation-model API such as Amazon Bedrock, Microsoft Foundry / Azure OpenAI, or Vertex AI; or deploy an open-weight model on GPU compute you control. Your application, retrieval layer, storage, identity and logs remain in your cloud boundary in both patterns.
- ASSESSMENT
The managed-API pattern can use closed-source frontier models without buying or operating GPUs. It is usually the fastest way to build a custom workflow, but prompts and retrieved context are processed by the managed service, so model availability, retention, abuse monitoring and regional routing must be checked for the exact feature and endpoint.
- ASSESSMENT
The private-model pattern gives more control over weights, serving and network paths, and can use managed endpoints or your own containers. It also makes your team responsible for capacity, patches, model upgrades, evaluation and failover.
- ASSESSMENT
The cloud provider becomes a data processor in either pattern: you need a DPA, a documented region, and an answer on cross-region routing and where support staff can access the environment from.
- RECOMMENDATION
Start with the managed-API pattern when the workflow is custom but model operations are not the source of competitive advantage; move to private model serving only if evaluation, volume, portability or the data boundary justifies the extra operations. Your stated technical capability is "unknown".
Self-hosted
STRONG ALTERNATIVEOpen-weight models on infrastructure you operate
- ASSESSMENT
Documents, queries and embeddings stay on machines you own, using an open-weight model whose licence you review. For a brief that involves confidential documents, special-category data and personal data, that removes a model-API vendor from the data path rather than governing that transfer by contract.
- ASSESSMENT
It costs you the operational work instead: a GPU server, Docker, Linux, backups and a patching routine. Your stated technical capability is "unknown", which is the attribute this option most depends on.
- ASSESSMENT
No processor agreement, subprocessor list or cross-border transfer assessment is needed for the model itself, because no third party processes the content.
- RECOMMENDATION
Recommended where local processing is preferred (you did not say so) and the content is sensitive (confidential documents, special-category data and personal data).
Enterprise SaaS
CONSIDER IFFinished closed-source cloud product with enterprise controls
- ASSESSMENT
This is a complete vendor application, not a model API: examples include an enterprise assistant, coding copilot or document product with the workflow, interface, connectors and administration already built. It can use closed-source cloud models while requiring no model hosting or application engineering from your team.
- RECOMMENDATION
Choose it when the product already performs the actual workflow and its controls meet your requirements. Do not choose it only because its underlying model is strong: a finished SaaS product is less flexible than building against a managed API when your process, integrations or review steps are organisation-specific.
- ASSESSMENT
Vendor commitments are treated as unverified until we have fetched the page that makes them. Until then this option carries questions to ask, not assurances: a signed data processing agreement covering the data you will actually put in; a documented data residency commitment naming the region, in the contract rather than a blog post; a written no-training commitment for your content, including uploads and connected sources; stated retention periods and a deletion path you can exercise; an administrative audit log you can export, and SSO with group-based access control.
- RECOMMENDATION
No jurisdiction was named, so this is the check rather than the conclusion: compare the vendor's stated processing locations and subprocessor list against the cross-border transfer rules wherever you operate before uploading anything.
03Deployment stacks
- Self-hostedDocument classification and OCR pipelineDocling (or Unstructured) converts and OCRs, embeddings plus rules classify, a local model reads only the documents that need reading, and Paperless-ngx gives the business a place to search and file. Starts CPU-only; a GPU is added when the volume justifies it.
- Self-hostedInvoice capture and extractionInvoices arrive by email or watched folder, Docling converts them (OCR included), a local vision-capable model fills a JSON schema under constrained decoding, code checks the arithmetic and the supplier against your master data, and n8n posts the clean ones while routing the rest to a person.
04Tools by hosting option
Self-hosted6
- LibraryDoclingDocument conversion toolkit that parses PDF, Office and image files into structured Markdown or JSON, preserving reading order, tables and figures for downstream retrieval.
- Model serverLocalAIDrop-in OpenAI-compatible API server that runs text, embedding, image and audio models locally across several back ends, including CPU-only deployments.
- LibraryPaddleOCROCR toolkit with detection, recognition and layout models, including Chinese and other East Asian scripts, plus table and formula recognition. Runs offline on CPU or GPU.
- Self-hostedpaperless-ngxSelf-hosted document management system that OCRs incoming scans and email, applies tags and correspondents through trainable rules, and keeps a searchable archive.
- LibraryTesseract OCRLong-established open-source OCR engine with trained data for many languages and scripts, usable offline and embedded in most self-hosted document pipelines.
- HybridUnstructuredLibrary and hosted API that partition documents of many formats into typed elements for indexing, with connectors to common storage systems and vector databases.
Private cloud8
- SaaSAmazon TextractAWS service extracting text, forms and tables from scanned documents, with specialised APIs for invoices, receipts and identity documents, priced per page.
- SaaSAzure AI Document IntelligenceAzure service for OCR, layout analysis, prebuilt document models and custom extraction, deployable in a chosen Azure region and available as a container for local processing.
- LibraryDoclingDocument conversion toolkit that parses PDF, Office and image files into structured Markdown or JSON, preserving reading order, tables and figures for downstream retrieval.
- SaaSGoogle Document AIGoogle Cloud service for OCR, form parsing and specialised document processors, with regional processing options and integration into the wider Google Cloud data stack.
- Model serverLocalAIDrop-in OpenAI-compatible API server that runs text, embedding, image and audio models locally across several back ends, including CPU-only deployments.
- SaaSNanonetsHosted document AI service with trainable extraction models, an approval interface and workflow integrations for accounts payable and back-office document types.
- SaaSRossumCloud platform for transactional document processing — invoices, orders, delivery notes — with extraction, a validation interface for exceptions and ERP integrations.
- HybridUnstructuredLibrary and hosted API that partition documents of many formats into typed elements for indexing, with connectors to common storage systems and vector databases.
Vendor cloud7
- SaaSAmazon TextractAWS service extracting text, forms and tables from scanned documents, with specialised APIs for invoices, receipts and identity documents, priced per page.
- SaaSAzure AI Document IntelligenceAzure service for OCR, layout analysis, prebuilt document models and custom extraction, deployable in a chosen Azure region and available as a container for local processing.
- SaaSGoogle Document AIGoogle Cloud service for OCR, form parsing and specialised document processors, with regional processing options and integration into the wider Google Cloud data stack.
- SaaSMindeeDocument parsing API with prebuilt models for invoices, receipts and identity documents, plus custom model training. European vendor with a developer-first API.
- SaaSNanonetsHosted document AI service with trainable extraction models, an approval interface and workflow integrations for accounts payable and back-office document types.
- SaaSRossumCloud platform for transactional document processing — invoices, orders, delivery notes — with extraction, a validation interface for exceptions and ERP integrations.
- HybridUnstructuredLibrary and hosted API that partition documents of many formats into typed elements for indexing, with connectors to common storage systems and vector databases.
On-premise (enterprise plan)4
- SaaSAzure AI Document IntelligenceAzure service for OCR, layout analysis, prebuilt document models and custom extraction, deployable in a chosen Azure region and available as a container for local processing.
- LibraryPaddleOCROCR toolkit with detection, recognition and layout models, including Chinese and other East Asian scripts, plus table and formula recognition. Runs offline on CPU or GPU.
- Self-hostedpaperless-ngxSelf-hosted document management system that OCRs incoming scans and email, applies tags and correspondents through trainable rules, and keeps a searchable archive.
- LibraryTesseract OCRLong-established open-source OCR engine with trained data for many languages and scripts, usable offline and embedded in most self-hosted document pipelines.
05Compliance hot spots
This use case usually raises personal data, sensitive data, data residency, retention, security, confidentiality, automated decision-making, sector rules.
- Sensitive data
- No published jurisdiction page names this topic yet
- Data residency
- No published jurisdiction page names this topic yet
- Retention
- Hong Kong
- Security
- China (mainland)
- Automated decision-making
- European UnionUnited KingdomSouth Korea
- Sector rules
- Hong Kong
06Example questions
Each of these opens the question box with the text already in it. The answer is researched for your organisation, not for this page.
07Related use cases
- DocumentsSpreadsheet analysisAsking questions of tabular data in natural language — totals, trends, outliers, reconciliations — and getting an answer with the calculation behind it. Works best when the model writes code that runs, rather than reading numbers itself.
- DocumentsRFP draftingAnswering tenders and security questionnaires from a library of previous answers. The work is retrieval plus reuse: most questions have been answered before, in a form somebody already approved.
- DocumentsReport generationProducing a recurring document — a management pack, client update, board summary, compliance return — from data and prior text. The template and the data are fixed; the narrative around them is what takes the time.
- DocumentsInvoice extractionReading supplier invoices — header fields, line items, tax, totals — into structured records. The extraction step only. What happens to the record afterwards is invoice processing automation.
- DocumentsDocument classificationSorting incoming documents into types and routing them — which team, which folder, which workflow, which retention rule. Usually the first step of a larger pipeline and the cheapest place to remove manual handling.
- DocumentsData extractionPulling defined fields out of unstructured text into a schema — from forms, reports, emails, filings or web pages. The general case of invoice extraction, and the step that turns documents into something a database can hold.
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