AWS Textract and Google Cloud Vision are both cloud OCR services you call by API, but they solve different problems. Textract is built to pull structured data out of forms, tables, invoices, and IDs. Google Vision reads text from any image and adds label, object, and logo detection, but it does not parse forms or tables into fields. DocuOCR is a ready-to-use alternative to both that returns finished, validated data.
Built for US teams choosing between the two cloud OCR APIs: see where each one fits, what both make you build, and how a finished product compares. Last updated July 2026.
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One of these structures documents, one reads text from any image, and one is the finished workflow. Here is the honest version of each.
Amazon's cloud document-extraction API. Detect Document Text handles OCR, while Analyze Document returns forms as key-value pairs, tables as rows and columns, plus Queries and signatures. Specialized Analyze Expense, Analyze ID, and Analyze Lending APIs handle invoices, IDs, and mortgage packages. It uses generalized models, integrates natively with S3, Lambda, and IAM, and is built to turn documents into structured data.
Google's general-purpose image analysis API. Its OCR features, TEXT_DETECTION and DOCUMENT_TEXT_DETECTION, return recognized text (including handwriting) with position and layout across many languages, and it also detects labels, objects, logos, faces, and landmarks. It reads the text on an image well, but it does not group that text into form fields or reconstruct tables, so structuring is left to you.
A ready-to-use intelligent document processing product, not a raw cloud API. It classifies a mixed batch, reads any layout, extracts the fields you define, validates them, routes low-confidence reads to a built-in review screen, and exports clean data through a dashboard and one REST API, with no AWS or Google Cloud account, IAM, service account, or pipeline to build.
All three read text from documents. The difference is whether you get structured fields back and how much you build around the engine. Sourced from the AWS and Google Cloud documentation and pricing pages, July 2026.
| Factor | Amazon Textract | Google Cloud Vision | DocuOCR |
|---|---|---|---|
| Type of tool | Cloud document-extraction API | General-purpose image OCR and analysis API | Ready-to-use product, plus REST API |
| Primary job | Structure documents into data | Read text from any image | Finished, validated document data |
| Who it is for | AWS-native developer teams | Developer teams needing raw text or image tagging | Business teams and developers |
| Getting started | AWS account, IAM, and a pipeline you build | GCP project, service account, and parsing you build | Sign in and process a document |
| Plain text OCR | Yes, Detect Document Text | Yes, TEXT_DETECTION and DOCUMENT_TEXT_DETECTION | Yes, on every page |
| Forms and key-value pairs | Yes, Analyze Document Forms | No, returns text and coordinates only | Yes, named fields you define |
| Tables | Yes, preserved as rows and columns | No, table text only, you rebuild structure | Yes, extracted to fields |
| Handwriting (ICR) | Yes, inside Analyze Document | Yes, DOCUMENT_TEXT_DETECTION | Yes, ICR on stamps and handwriting |
| Invoices and receipts | Yes, Analyze Expense | Text only, you parse it | Built in, by schema |
| Non-document image tasks | No | Yes, labels, objects, logos, faces | No, documents only |
| Classify a mixed batch | Build your own routing | Build your own routing | Built in, sorts the file for you |
| Human review of low-confidence reads | Build your own screen | Build your own screen | Included review screen |
| On-premises option | No, AWS cloud only | No, Google Cloud only | Ask us about deployment |
| Free to test | 1,000 pages per month for 3 months | 1,000 units per month per feature, ongoing | Free on your own files, no signup |
| Pricing model | Per page by feature, tiered by volume | Per 1,000 units by feature, cheaper past 5M | Per page, the pipeline included, no seats |
Pricing for both cloud services changes by region and volume, so confirm exact rates on the current AWS Textract and Google Cloud Vision pricing pages before you commit. As of July 2026 Vision text detection is free for the first 1,000 units a month, then around $1.50 per 1,000 units and about $0.60 per 1,000 above five million, while Textract plain OCR is around $1.50 per 1,000 pages and its forms and tables cost more (Forms about $50 per 1,000, Tables about $15 per 1,000). If you only need the text, Vision is a strong, cheap choice; if you need structured fields, Textract does more of the work. If you want a working process today, DocuOCR is built on intelligent document processing that classifies, reads, extracts, validates, and exports, so your team reviews data instead of assembling it.
Each service has real advantages. The point of a comparison is to match those to your stack and documents, not to crown a winner.
Trade-off: there is no custom model training, it is cloud-only inside AWS, forms and tables cost more per page, and the classification, review, validation, and export around the API are yours to build and run.
Trade-off: Vision does not return form key-value pairs or table structure, so turning a document into fields is code you write on top of it; it is cloud-only inside Google Cloud, and the surrounding workflow is still yours to build.
Whichever cloud API you pick, recognition is the first step. These are the pieces a finished product includes that a raw API does not.
Both work one image or document at a time. Sorting a stack of different document types and routing each to the right extraction is code you write and maintain yourself.
Neither ships a finished screen where a person corrects a low-confidence value before it lands in your system. You build the review interface and the queue.
Checking that a total adds up, a date is valid, or an ID matches a pattern happens in your application logic, not in the OCR call.
Vision hands back text and coordinates and Textract hands back entities and tables; turning either into the named fields your system expects is mapping code you own.
Getting clean data into a spreadsheet, database, or downstream system is an integration you build and host on top of the API.
You run the pipeline: the storage, the retries, the monitoring, the IAM or service account, and the maintenance as volumes and formats change.
DocuOCR includes all six. It classifies the file, reads any layout, extracts the fields you define, validates them, routes uncertain reads to a built-in review screen, and exports clean data, so you adopt a workflow instead of building one around a recognition API.
Classify, read, extract, validate. Drop a file in and the whole sequence runs on its own, with no AWS or Google Cloud pipeline behind it.
The engine reads a mixed batch and sorts it by document type, so the right extraction runs on each one without anyone separating the stack first.
OCR and ICR convert PDFs, photos, faxes, and scans into machine-readable text, including handwriting and stamps that a raw OCR call can miss.
DocuOCR pulls the values tied to their labels and returns the fields you defined, so you get structured data instead of text and bounding boxes to parse.
Values run through your rules, low-confidence reads route to review, and clean data exports to a spreadsheet or your systems by API, with an audit trail.
# invoice.pdf -> extracted data (not just text) { "doc_type": "invoice", "vendor": "Lakeside Supply Co", "invoice_number":"INV-20418", "invoice_date": "2026-05-22", "total": "4820.00", "confidence": 0.98 } # classified, read, validated, ready for export
A short decision guide based on your documents, your stack, and whether you want an API or a finished product.
You need structured data out of forms, tables, invoices, or IDs, you build on AWS, or you process mortgage packages with Analyze Lending.
You mainly need accurate text from photos, scans, or mixed images, you want image tagging alongside OCR, or you process very high volumes on Google Cloud.
You want finished, validated fields instead of an API to build around, business users plus developers both need access, and you would rather test on your own files than wire up a cloud project.
With Textract or Vision you call recognition, then build classification, field mapping, validation, and storage around it on a cloud account. With DocuOCR you post a document to a single endpoint and get back the classified type, the recognized text, and the extracted fields, with a confidence score on every value, ready to use.
# classify + extract in one request curl https://api.docuocr.com/v1/extract \ -H "Authorization: Bearer $KEY" \ -F "file=@scanned_document.pdf" \ -F "classify=true" # -> doc type + named fields + confidence
The questions teams ask most when they compare the two cloud OCR services and a ready-to-use alternative.
Amazon Textract is built for structured document extraction: it returns key-value pairs from forms, preserves tables, and reads invoices, IDs, and lending packages. Google Cloud Vision is a general-purpose image API whose OCR returns recognized text and coordinates but not parsed forms, tables, or key-value fields. Textract structures documents; Vision reads text from any image.
Neither is universally better; it depends on the job. Pick Amazon Textract if you need structured data out of forms, tables, invoices, or IDs. Pick Google Vision if you need raw text from photos, scans, or mixed images, or you also want labels, logos, and object detection. If you want finished, validated fields instead of an API, DocuOCR fits better than either.
No. Google Cloud Vision OCR (TEXT_DETECTION and DOCUMENT_TEXT_DETECTION) returns the recognized text and its position on the page, but it does not group that text into form key-value pairs or reconstruct table rows and columns. Parsing a form or table into structured fields is code you write on top of Vision, or the job of Amazon Textract or Document AI instead.
For plain OCR they are close, and Vision can be cheaper at scale. As of July 2026 Vision text detection is free for the first 1,000 units a month, then around $1.50 per 1,000 and about $0.60 per 1,000 above five million. Textract plain OCR is around $1.50 per 1,000 pages, but forms and tables cost far more, so Textract is pricier when you need structure.
Yes. Google Cloud Vision's DOCUMENT_TEXT_DETECTION handles dense text and handwriting across many languages and returns it as recognized text with layout. Amazon Textract also reads handwriting inside its Analyze Document API. Accuracy on either depends on the legibility of the writing, so test both on your own samples rather than trusting a headline number.
Amazon Textract works better for invoices out of the box because its Analyze Expense API returns named fields like vendor, date, total, and line items. Google Vision returns the invoice text and its position, leaving you to locate and label each value yourself. For a finished invoice workflow with review and export, a ready-to-use product like DocuOCR removes that parsing work entirely.
No. Both are cloud-only services: Textract runs inside AWS regions and Vision runs inside Google Cloud. Neither offers an on-premises deployment. If your documents must stay inside your own network, that rules out both raw APIs and points you toward a deployment-flexible option instead.
Yes, in most cases. Both are developer services reached through a REST API or client library, and turning their output into a working process means writing code for classification, field mapping, review, validation, and export on a cloud account. You can test a sample in each console, but production use on either is an engineering project.
A good alternative to both is a ready-to-use intelligent document processing product that includes the workflow the cloud APIs leave you to build. DocuOCR classifies a mixed batch, extracts the fields you define, validates them, sends low-confidence reads to review, and exports clean data through a dashboard and one REST API, with no AWS or Google Cloud account, IAM, or service account to manage.
On plain printed text both are strong, and Vision is well regarded for general OCR on clean documents. On structured forms and tables Textract is usually more useful because it also returns the structure, not just the characters. Accuracy depends on your document types, so run both, and a ready-to-use option, on your own files and measure the result.
Skip the AWS pipeline and get finished data from a product that includes classification, review, and export.
Compare the other Google document service, Document AI, and a ready-to-use product that classifies, reads, validates, and exports.
The head-to-head for Google's document-extraction platform, with custom-trained extractors and prebuilt processors compared.
The Microsoft and Amazon pairing, with custom training and an on-premises container compared.
The single REST call that replaces a Textract or Vision pipeline, returning classified type, text, and named fields.
An honest roundup of the leading intelligent document processing tools and the buyer each one fits.
The full platform behind the comparison, with a dashboard for teams who want document data without code.
Run the same file you planned to test on Textract or Google Vision through DocuOCR, watch it classify, read, and return named fields, then connect the API to process every document that follows on its own.
