What is PDF & OCR?
PDF & OCR extracts text, tables, and structured data from PDF documents — whether they contain native digital text or scanned images of paper documents. This feature turns documents that are locked in PDF format into clean, structured data you can search, analyze, and use in workflows.
Many business processes still revolve around PDFs: invoices, contracts, reports, receipts, government filings, medical records. Getting data out of these documents manually is tedious and error-prone. Autonoly automates the entire extraction pipeline. If you are building end-to-end data workflows, this feature pairs naturally with our no-code automation guide for designing extraction pipelines without writing code.
Native PDF Extraction
For PDFs with embedded text (the kind you can select and copy), extraction is fast and precise. The system preserves the document's structure — paragraphs, headings, tables, lists — and outputs clean text with formatting intact. Metadata such as author, creation date, and page count is also extracted and available as workflow variables.
OCR for Scanned Documents
Scanned PDFs are essentially images wrapped in a PDF container. There's no text to select — just pixels. OCR (Optical Character Recognition) analyzes the image and converts visual text back into digital characters. Autonoly's OCR engine supports:
100+ languages including Latin, Cyrillic, CJK, Arabic, and Hebrew scripts
Mixed-language documents where different sections use different languages
Handwritten text with AI-powered handwriting recognition
Low-quality scans with noise reduction and image enhancement
Rotated and skewed pages — automatic orientation correction before recognition
The OCR engine runs in a secure, isolated environment and supports both CPU and GPU acceleration depending on your plan tier, which significantly speeds up processing of large document batches.
Table Extraction
Tables are one of the hardest elements to extract from PDFs because their structure is purely visual. The PDF format doesn't have a "table" concept — it's just text positioned at specific coordinates. Autonoly uses AI to:
Detect table boundaries and column/row structures
Handle merged cells, multi-line cells, and nested tables
Preserve header relationships so data makes sense
Output tables as structured rows and columns ready for spreadsheets
Differentiate between multiple tables on the same page
Key-Value Pair Detection
Many documents contain form-like data — labeled fields with values. Invoice number, date, total amount, customer name. The AI identifies these patterns and extracts them as structured key-value pairs, making it easy to feed the data into Data Processing pipelines or store it in a database.
For recurring document formats, you can train the extraction model by providing a few annotated examples. After that, the system recognizes the layout automatically and applies the same extraction rules to every new document with the same structure.
Batch Processing
Process hundreds or thousands of PDFs in a single workflow. Upload a folder of invoices and extract line items from each one. Point to a directory of contracts and pull out key terms and dates. Batch processing uses parallel execution to handle large document volumes efficiently.
The batch processor provides a progress dashboard showing the status of each document — queued, processing, completed, or failed. Failed documents are retried automatically, and persistent failures are flagged for manual review without blocking the rest of the batch.
Integration with Workflows
PDF extraction integrates seamlessly with the rest of Autonoly:
[Browser Automation](/features/browser-automation) — download PDFs from websites automatically, then extract their contents
[Data Processing](/features/data-processing) — clean and transform extracted data
[Integrations](/features/integrations) — send extracted data to Google Sheets, Airtable, or any connected tool
[AI Vision](/features/ai-vision) — use vision models to understand complex document layouts
[Email Campaigns](/features/email-campaigns) — extract data from PDF attachments in incoming emails
[SSH Terminal](/features/ssh-terminal) — process PDFs stored on remote servers without downloading them locally first
Accuracy and Validation
OCR accuracy depends on document quality, but Autonoly maximizes it through:
Pre-processing — automatic image enhancement, deskewing, and noise removal
Confidence scores — each extracted value includes a confidence score so you can flag low-certainty results
Human review queue — route low-confidence extractions to a human for verification
Template matching — for recurring document types (like monthly invoices from the same vendor), define a template once and extract consistently
Best Practices
Use templates for recurring document types. If you process the same kind of document regularly — monthly invoices from a supplier, weekly reports from a partner — define a template once. The system will apply the same extraction rules automatically, which improves accuracy and speed dramatically.
Pre-sort documents by type before batch processing. When you have a mixed batch of invoices, contracts, and receipts, grouping them by type allows you to apply specific extraction rules to each group. This produces cleaner results than running a single generic extraction across all document types.
Check confidence scores and set thresholds. Instead of reviewing every extraction manually, set a confidence threshold (e.g., 95%) and only review documents that fall below it. This focuses human effort where it is most needed and lets high-confidence results flow through automatically.
Combine OCR with AI Vision for complex layouts. Documents with unusual formatting — multi-column layouts, overlapping text, or embedded charts — benefit from the combined power of OCR and AI Vision. OCR extracts the text while AI Vision interprets the spatial relationships between elements.
Store extraction results in a database for historical analysis. Instead of exporting to one-off spreadsheets, write extraction results to a database with timestamps. Over time, this builds a searchable archive that supports trend analysis and auditing. Learn about connecting databases in our automate Google Sheets guide, which covers similar data pipeline patterns.
Security & Compliance
Document processing is a sensitive operation. PDFs often contain personal data, financial records, or confidential business information. Autonoly processes all documents in isolated, ephemeral containers that are destroyed after extraction completes. No document content is retained on Autonoly's servers beyond the active workflow session unless you explicitly save results to your own storage.
All file uploads and extractions are encrypted in transit (TLS) and at rest (AES-256). For organizations subject to GDPR, HIPAA, or SOC 2 requirements, Autonoly provides data processing agreements and can be configured to process documents in specific geographic regions. The credential vault used for password-protected PDFs follows the same encryption standards used across all Autonoly security features.
Audit logs track every document processed, including who initiated the extraction, which workflow was used, and where the results were sent. These logs are tamper-proof and can be exported for compliance reviews.
Common Use Cases
Accounts Payable Automation
A finance team receives hundreds of vendor invoices each month as PDF attachments. Instead of manually keying data into their accounting system, they set up an Autonoly workflow that monitors an email inbox, extracts invoice PDFs, pulls out line items, amounts, dates, and vendor information, validates the data against purchase orders in a database, and writes approved invoices directly into their accounting software via API & HTTP. Discrepancies are flagged for human review. The process that used to take two full-time employees now runs unattended with a weekly exception review.
Legal Contract Review
A legal team needs to review hundreds of contracts for specific clauses — termination terms, liability caps, and renewal dates. They upload contract PDFs in batches, and the extraction workflow pulls out key-value pairs for each clause type. The results are compiled into a structured spreadsheet with links back to the source documents. Lawyers review the summary instead of reading every page, cutting review time by over 80%. For contracts received as scanned copies, OCR handles the text recognition before clause extraction begins.
Medical Records Digitization
A healthcare organization digitizes patient intake forms submitted on paper. Scanned forms are processed through OCR with handwriting recognition enabled, extracting patient name, date of birth, insurance information, and medical history fields. The extracted data flows into the electronic health records system through the Integrations feature. Confidence scores flag any field where the handwriting was ambiguous, routing those records to a human reviewer before the data enters the system.
Real Estate Document Processing
A real estate firm processes property listings, appraisal reports, and title documents daily. PDFs are downloaded automatically from county recorder websites using Browser Automation, then the extraction pipeline pulls out property addresses, assessed values, ownership history, and legal descriptions. The structured data feeds into a PostgreSQL database for search and analysis. This workflow, covered in more detail in our real estate automation guide, turns hours of manual research into an automated pipeline.
See pricing for PDF processing limits and OCR language availability per plan.