DocuOCR reads documents by the thousand, not one at a time. Send a folder, a zip, or an API stream of PDFs, scans, and images, and the engine classifies the batch, runs OCR where the text is not selectable, extracts the structured fields from every file in parallel, and returns clean data you export to Excel, CSV, JSON, or push straight into your system. No template, no file-by-file clicking.
Automated, API-driven OCR built for teams that process documents at volume.
Last updated June 2026
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Drop in a batch of documents to see the structured data DocuOCR pulls from each one, ready to export.
Send the whole batch, let the engine read and extract every file, review only what it flags, then export the lot. No opening documents one by one, no keying data into a spreadsheet for days.
Drop a folder or zip of PDFs, scans, and images into the dashboard, or queue documents through the REST API. One job, however many files.
DocuOCR sorts a mixed batch by document type, runs OCR on anything scanned or photographed, and reads every file at once instead of in sequence.
The right fields are pulled from each document by type, with a confidence score on every value so low-confidence reads in a large batch surface for review.
Send the structured results to Excel, CSV, or JSON in one download, or stream them into your ERP, document system, or data pipeline through the API.
# 1 job, 2,140 files -> one structured result set { "batch_id": "feb-intake-2026", "documents": 2140, "classified": { "invoice": 1190, "statement": 640, "form": 310 }, "results": [ { "file": "inv_00412.pdf", "type": "invoice", "total": "4,820.00", "confidence": 0.98 }, { "file": "stmt_00097.pdf", "type": "statement", "flagged": true, "confidence": 0.71 } ], "for_review": 38 } # export -> .xlsx | .csv | .json | ERP / data pipeline
Reading one document well is easy. Reading ten thousand without a person babysitting each one needs classification, parallel processing, and a way to catch the few that need a second look.
Batch OCR is one way to run DocuOCR. For a single file there is the same engine behind the OCR software, and to wire bulk processing into your own product use the OCR API.
When the inbox or the scan folder fills faster than a person can key the data, batch OCR is what clears it.
Clear high-volume intake queues of invoices, statements, and forms without adding headcount to key data by hand.
Turn a backlog of scanned paper, a records room, or a migrated archive into structured, searchable data in bulk.
Feed clean extracted data to your automation and ERP instead of building OCR and classification into every bot.
Get structured fields out of thousands of documents so the data can land in a warehouse or report, not a PDF pile.
Process client document volumes per page across many accounts, with an audit trail and review built in.
Add high-volume document extraction to your application through one REST API instead of running an OCR stack yourself.
Reading a single document by hand or with a one-off tool is fine until the volume climbs. At a few hundred a week, the file-by-file routine becomes a person opening each document, retyping the fields, and saving the result, slowly and with the occasional typo nobody catches.
Batch OCR collapses that into one job. DocuOCR classifies the batch, reads every file in parallel, flags only the uncertain ones, and exports the whole set, so the work scales with the engine instead of with how many people you can hire to key data.
See the full document processing pipelineAccuracy does not change because the batch is large. DocuOCR extracts at 95 to 99 percent field-level accuracy on clean documents, and a confidence score plus review on every file means a doubtful read in a batch of thousands is checked rather than trusted blindly. You stay in control of quality without reading every page.
Batch is the scale mode. Underneath it is the same platform that classifies, reads, extracts, and validates whatever you send.
The core OCR platform for reading printed, typed, and handwritten text from any document, one file or many.
Batch processing plus the governance layer large teams need: SSO, role-based access, audit logs, and an SLA.
The end-to-end pipeline that classifies, reads, extracts, and validates documents before the data leaves the system.
The dashboard product for teams who want structured data out of documents without writing code.
Sort a mixed batch into invoices, statements, contracts, and forms automatically before extraction runs.
Replace manual keying across high document volumes with extraction that writes straight to your system.
Queue bulk volumes and get structured JSON back through one REST call, built into your own product.
Planning a large run? Read how to OCR thousands of documents at once for the step-by-step on preparing a batch, handling mixed document types, and keeping accuracy high at volume.
A batch can carry financial records, personal data, and confidential business documents, so every file is handled under enterprise-grade controls, with encryption in transit and at rest, role-based access, audit logs across the batch, and optional automatic purge after processing. Ask about deployment options for your environment.
The questions people ask most about running OCR and data extraction across documents in bulk.
Batch OCR is the process of running optical character recognition and data extraction across many documents in a single automated job instead of one file at a time. You send a folder, a zip, or a stream of files through an API, and the software OCRs each one, pulls out the structured data, and returns it together, with no manual click-through per document.
Batch OCR software is a tool built to read and extract data from large volumes of documents automatically. It watches a folder or accepts an API call, classifies a mixed batch, runs OCR on scans and PDFs, extracts the fields you need from every file in parallel, and exports the results to Excel, CSV, JSON, or your downstream system without a person processing each page.
You OCR multiple documents at once by uploading them as a batch or sending them through an API rather than opening each file. DocuOCR accepts a folder or zip of PDFs, scans, and images, processes them in parallel, applies OCR where the text is not selectable, extracts the data from each, and returns one structured result set you can export in a single step.
There is no fixed cap. DocuOCR is built to process documents in bulk, from a few dozen in a folder to tens of thousands queued through the API, processing them in parallel so a large batch finishes in a fraction of the time a person would take. Throughput scales with your plan, and you pay per page for what you actually run.
Single-document OCR reads one file at a time, which is fine for the occasional contract or invoice. Batch OCR automates the same extraction across hundreds or thousands of documents in one unattended job, with classification for mixed batches, parallel processing, confidence scoring, and bulk export. The accuracy per document is the same; batch OCR removes the manual, file-by-file work.
Yes. DocuOCR classifies each file in a mixed batch before extraction, so a single folder of invoices, contracts, statements, and forms is sorted by type and the right fields are pulled from each. You do not have to separate documents by type first or run a different job per format, which is what makes high-volume processing practical.
Yes. You can send documents to DocuOCR through one REST API, queue large volumes programmatically, and receive structured JSON back for each one, so batch OCR plugs into your ERP, document management system, or data pipeline. Teams that prefer no code can drop a folder or zip into the dashboard instead and export the results to a spreadsheet.
Accuracy does not drop because the volume is high. DocuOCR extracts at roughly 95 percent field-level accuracy on clean documents and climbs toward 99 percent with validation rules and review. Every field carries a confidence score, so a low-confidence read in a batch of ten thousand is flagged for a reviewer instead of passing through silently.
DocuOCR is priced per page, so a batch of one thousand pages costs the same per page as a batch of ten, and you pay only for the documents you process rather than a fixed enterprise license. You can test it on your own batch for free before committing, and pricing scales down as monthly volume goes up.
Drop in a folder of documents, watch the structured data come back from every file, then scale per page across the whole volume.
