DocuOCR is an OCR API and document data extraction API in one. POST a PDF, scan, or image and get back clean JSON: named fields, table rows, and confidence scores. No OCR engine to host, no templates to build, just a REST call.
Built for US engineering and operations teams automating high-volume document workflows.
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An OCR API is a service you call from your own code to read documents. Instead of installing an OCR library, training it, and keeping it running, you send a file to an HTTPS endpoint and get the recognized content back as a structured response. The work of recognizing characters, parsing layout, and returning data happens on the provider's side, so your app stays light.
The difference that matters in 2026 is what the API gives back. A plain OCR API returns raw text in reading order, which still leaves you to hunt for the invoice total or the account number. A document data extraction API, which is what DocuOCR provides, pairs OCR with AI: it classifies the document, finds the fields you asked for, keeps tables and line items intact, scores its confidence, and hands you a finished JSON record. One request replaces a recognition library plus all the parsing code you would otherwise maintain.
Because everything moves over REST and JSON, an OCR API fits any stack. A Python data pipeline, a Node.js backend, a PHP web app, or a Go microservice can all post a document and read the result the same way. That is why teams reach for an API instead of a desktop tool once document volume grows past what a person should be retyping.
Authenticate, upload, extract. The whole round trip is JSON, and a webhook can deliver the result so you never poll.
Pass your API key as a Bearer token in the Authorization header. Generate keys from your dashboard.
POST a PDF, PNG, JPG, TIFF, or WEBP to the upload endpoint and receive a file_id.
POST the file_id to the extract endpoint to start an async job and get an extraction_hash.
Poll the extraction endpoint or register a webhook to receive fields, tables, and confidence scores.
# 1. Upload a document curl -X POST https://docuocr.com/api/documents/upload \ -H "Authorization: Bearer YOUR_API_KEY" \ -F [email protected] # 2. Start extraction with the returned file_id curl -X POST https://docuocr.com/api/documents/extract \ -H "Authorization: Bearer YOUR_API_KEY" \ -d '{"file_id":"f_123"}' # 3. Fetch the structured result curl https://docuocr.com/api/documents/extraction/abc123 \ -H "Authorization: Bearer YOUR_API_KEY" # -> { "fields": { "total": "1240.00", ... }, # "line_items": [ ... ], "confidence": 0.98 }
See the full reference, parameters, and response schema in the DocuOCR API documentation.
Every endpoint returns structured JSON you can drop straight into a database or workflow.
Ask for the invoice number, date, vendor, or total and get those exact fields, not a paragraph of text to parse.
Rows and columns come back as arrays with structure intact, so a 40-line invoice stays a 40-line table.
Every field carries a score, so you can auto-approve high-confidence values and route the rest for review.
Register an HTTPS URL and DocuOCR posts the finished payload to you the moment a job completes.
Push thousands of documents through the same endpoint without blocking, then collect results as they finish.
Target a built-in type or your own custom template by id to control exactly which schema you get back.
Six things separate an OCR API you ship on from one you rip out after the trial.
Confirm the API returns named fields and tables, not a raw text dump you still have to parse with regex.
Test on your own documents and measure accuracy per field. A high character accuracy means little if the total is wrong.
You need a score per field and rule checks so bad values get flagged instead of silently corrupting your data.
For volume, the API must queue jobs and call you back. Synchronous-only APIs stall the moment files get large.
Look for clear per-page rates and a free tier to test, not opaque enterprise quotes or per-seat licensing.
SOC 2, encryption in transit and at rest, optional zero retention, and HIPAA-ready handling for sensitive files.
Both read the page. Only one hands you data you can use without writing a parser.
| Factor | Plain OCR API | DocuOCR extraction API |
|---|---|---|
| What you get back | Raw text in reading order | Named fields, tables, and JSON |
| Parsing work | You write the parser | None, the schema is structured |
| Tables and line items | Flattened into text | Kept as rows and columns |
| Confidence per field | Rarely | Yes, on every field |
| Document classification | No | Yes, the type is detected |
| High volume | Sync, blocks on big files | Async jobs and webhooks |
| Time to production | Days of parsing code | One integration, ship same day |
Any product or pipeline that touches documents can call the API instead of paying people to retype them.
POST incoming invoices and post the returned fields straight into QuickBooks, NetSuite, or your ERP.
Read bank statements, pay stubs, and tax forms inside your underwriting flow to approve applicants faster.
Add a document upload feature to your app and let the API turn files into data behind the scenes.
Drop the API into a UiPath, Power Automate, or custom bot so a document triggers the rest of the process.
Extract ACORD forms and claims at volume and route low-confidence fields to a reviewer automatically.
Run an archive of scanned PDFs through batch extraction to populate a database without manual entry.
No seat licenses and no setup fees. Start free to validate accuracy on your own documents, then pay per page as your volume grows. Higher volumes move to committed plans with lower per-page rates and priority throughput.
The questions developers and buyers ask most when choosing an OCR API.
An OCR API is a web service you call from your own code to turn document images and PDFs into machine-readable data. You send a file over HTTP, the API runs optical character recognition and AI extraction on it, and it returns structured results such as text, fields, tables, and confidence scores as JSON. It lets developers add document reading to an app without building or hosting an OCR engine.
Most OCR APIs price per page or per document rather than per user. Basic text recognition from the large cloud providers runs around $1.00 to $1.50 per 1,000 pages, while table and form extraction costs more. DocuOCR starts free so you can test on real documents, then scales on a per-page basis, so you pay only for the pages you process with no seat licenses or setup fees.
The best OCR API depends on your documents. For plain text, the big cloud vision services are fine. For business documents where you need named fields, line items, and validation rather than a wall of text, pick an API built for document data extraction with confidence scores, webhooks, and a real JSON schema. Test two or three on your own files and compare field-level accuracy before you commit.
An OCR API converts an image into raw text and leaves you to find the values you need. A document data extraction API adds an AI layer that classifies the document, locates specific fields, reads tables and line items, validates them, and returns structured records ready for your database. OCR is one step inside extraction. DocuOCR does both in a single call.
Integration takes three calls. Authenticate with a Bearer API key, POST your document to the upload endpoint to get a file_id, then POST that id to the extract endpoint and either poll for the result or register a webhook to receive it. The response is JSON, so any language that can make an HTTPS request, including Python, Node.js, PHP, Java, and Go, can use it.
Yes. A document OCR API reads native PDFs, scanned PDFs, and multi-page PDFs, along with PNG, JPG, TIFF, and WEBP images. For digital PDFs it reads the embedded text directly, and for scans it runs OCR on the page image. DocuOCR handles all of these in one endpoint and returns the same structured JSON regardless of how the PDF was created.
On clean, well-scanned business documents a modern AI OCR API reaches 95 to 99 percent field-level accuracy. Accuracy depends on image quality, document type, and whether the API uses plain OCR or AI extraction with validation. Confidence scores on every field let you route low-confidence values to a quick human review instead of trusting them blindly, which keeps a high-volume pipeline reliable.
Several OCR APIs offer a free tier with a monthly page cap, which is enough for testing or very low volume. For production work the free tiers usually limit pages, speed, and accuracy, so most teams move to a paid plan once they go live. DocuOCR lets you start free to validate accuracy on your documents, then scale per page as your volume grows.
An honest side-by-side of the leading OCR APIs on type, pricing, and output, so you pick the right one for your build.
What Azure, AWS Textract, and Google Document AI each charge per 1,000 pages, and the fees the per-page rate hides.
Call the API from Python with a few requests calls, plus every OCR API's Python package compared.
Call the API from Node.js with the built-in fetch, plus every OCR API's npm package compared.
Call the API from Java with the built-in HttpClient, plus every OCR API's Maven artifact compared.
Call the API from PHP or Laravel with cURL or Guzzle, plus every OCR API's PHP package compared.
Call the API from .NET with the built-in HttpClient, plus every OCR API's NuGet package compared.
Extract text and data from any image, photo or screenshot with one REST API, in any language.
Turn resume PDFs and Word files into structured candidate JSON: contact, experience, education and skills.
Pull tables from digital and scanned PDFs to Excel, CSV or JSON with the rows and columns intact.
Convert a native or scanned PDF to structured JSON with named fields, tables and confidence.
The full platform behind the API, with a dashboard for teams who do not want to code.
How capture, classification, extraction, and validation fit into one IDP workflow.
Pull invoice numbers, dates, line items, and totals from any supplier layout.
Connect extraction to QuickBooks, NetSuite, and the tools your team already uses.
Run the API over a whole backlog: batch, queue, and pull structured data from every page in one job.
Where plain OCR stops and structured data extraction begins, and why an API that returns named fields matters.
Start free and scale per page. Pay only for the pages you process.
Generate an API key, POST your first document, and read structured JSON back in minutes. Scale per page when you go live.
