📄 PDF to JSON Parser

🚀 Convert PDFs into structured JSON in seconds. Upload any PDF and get clean, queryable fields. Optional field selection and custom prompts. No coding, no manual data entry.

🕒 Last updated: 2026-05-08 · 📊 Per-page parsing · 🧠 AI-driven extraction · 🚫 No auth required

Convert PDF documents into clean, structured JSON without writing custom parsers per document type. Upload one or more PDFs, optionally tell the actor which fields to extract, and the AI processes every page and returns one record per document with the extracted fields plus full page text. Built for invoice automation, contract review, research-paper indexing, regulatory filings, and any workflow that turns scanned or born-digital PDFs into queryable data.

The output is a structured record per file: a back-reference to the source PDF, the document name, the number of pages, a topic summary, a timestamp, and the extracted fields under fetchedData. Hand the dataset off to your database, BI tool, or AI pipeline. Every run is processed live with no caching of input PDFs.

👥 Built for	🎯 Primary use cases
Finance and AP teams	Auto-extract invoice fields into accounting systems
Legal and contract ops	Pull key terms, dates, parties from contracts
Research and academia	Index research papers for full-text search
Compliance and regulatory	Convert filings into queryable records
HR and recruiting	Parse resumes into structured candidate profiles
Data and engineering teams	Replace bespoke PDF parsers across products

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📋 What the PDF to JSON Parser does

• 📄 Multi-PDF input. Upload one or more PDFs via file upload or URL.
• 🧠 Smart extraction. Optionally specify the exact fields you want, or let the AI pick the important ones.
• ✏️ Custom prompts. Pass a system prompt to bias extraction toward your domain (legal, medical, financial, etc.).
• 📊 Page-aware. All pages of every PDF are processed before parsing, so nothing is lost.
• 🆔 Back-reference. Every record links back to the original PDF in the dataset.
• ⏱️ Timestamp. Every record carries a timestamp so you can rebuild a timeline.

The actor processes uploads in the order you provide them. Records stream into the dataset as parsing completes, so you can start consuming results before the run is fully finished. Ideal for workflows that need clean structured data from inconsistent PDF layouts.

💡 Why it matters: PDFs are the universal data format that nobody wants to parse. Bespoke parsers break with every layout change. AI-driven extraction adapts to layout variation without code changes, so finance, legal, and research teams can get from "PDF inbox" to "structured database" in minutes.

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🎬 Full Demo

🚧 Coming soon: a 3-minute walkthrough showing PDF upload, custom field extraction, and how to feed the output into Google Sheets via Apify integrations.

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⚙️ Input

Field	Type	Name	Description
pdfFile	array of strings	PDF File	Required. One or more PDF file URLs (uploaded via file upload or pre-existing URLs).
fieldsToExtract	string	Fields to Extract	Optional. Comma-separated list of fields (e.g. title, author, date, total, vendor). Empty = auto-detect.
systemPrompt	string	System Prompt	Optional custom prompt to bias the extraction toward your domain. Empty = smart default.
maxItems	integer	Max Items	Free users: limited to 10 items (preview). Paid users: optional, max 1,000,000.

Example 1. Extract specific fields from invoices.

{
  "pdfFile": [
    "https://example.com/invoices/INV-1001.pdf",
    "https://example.com/invoices/INV-1002.pdf"
  ],
  "fieldsToExtract": "vendor, invoiceNumber, date, dueDate, lineItems, total, currency"
}

Example 2. Domain-specific extraction with custom prompt (legal contracts).

{
  "pdfFile": [
    "https://example.com/contracts/MSA-2026.pdf"
  ],
  "fieldsToExtract": "parties, effectiveDate, termLength, autoRenewal, governingLaw, terminationClauses",
  "systemPrompt": "You are a contract analyst. Extract the requested fields verbatim from the agreement, preserving dates and numerical values exactly."
}

⚠️ Good to Know: when fieldsToExtract is set, the AI prioritizes those fields. When it is empty, the AI infers what is meaningful from the PDF and returns whatever it finds.

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📊 Output

The dataset returns one structured record per PDF. Each record carries the document name, page count, topic, timestamp, and a fetchedData object with the extracted fields. Consume the dataset as JSON, CSV, Excel, XML, or RSS via the Apify console or API.

🧾 Schema

Field	Type	Example
📄 documentName	string	INV-1001.pdf
📊 numberOfPages	number	2
🏷️ topic	string	Vendor invoice
📅 timestamp	ISO datetime	2026-05-08T12:00:00.000Z
📦 fetchedData	object	{ "vendor": "Acme Corp", "invoiceNumber": "INV-1001", ... }
🔗 sourceUrl	string (url)	https://example.com/invoices/INV-1001.pdf
❗ error	string or null	null

📦 Sample records

1. Typical record (invoice with custom fields)

{
  "documentName": "INV-1001.pdf",
  "numberOfPages": 2,
  "topic": "Vendor invoice",
  "timestamp": "2026-05-08T12:00:00.000Z",
  "fetchedData": {
    "vendor": "Acme Corp",
    "invoiceNumber": "INV-1001",
    "date": "2026-04-30",
    "dueDate": "2026-05-30",
    "lineItems": [
      {"description": "Cloud services Q2", "amount": 1200},
      {"description": "Support add-on", "amount": 300}
    ],
    "total": 1500,
    "currency": "USD"
  },
  "sourceUrl": "https://example.com/invoices/INV-1001.pdf",
  "error": null
}

2. Auto-detected fields (no fieldsToExtract specified)

{
  "documentName": "research-paper.pdf",
  "numberOfPages": 18,
  "topic": "Research paper",
  "timestamp": "2026-05-08T12:00:00.000Z",
  "fetchedData": {
    "title": "Diffusion-based generative models for tabular data",
    "authors": ["Jane Doe", "Carlos Lee"],
    "abstract": "We present a diffusion-based approach...",
    "keywords": ["diffusion", "tabular", "generative"],
    "publicationYear": 2026,
    "doi": "10.1234/abcd.5678"
  },
  "sourceUrl": "https://example.com/papers/diffusion-2026.pdf",
  "error": null
}

3. Failed parse (corrupt PDF)

{
  "documentName": "broken-file.pdf",
  "numberOfPages": null,
  "topic": null,
  "timestamp": "2026-05-08T12:00:00.000Z",
  "fetchedData": null,
  "sourceUrl": "https://example.com/broken-file.pdf",
  "error": "Could not parse PDF: file is encrypted"
}

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✨ Why choose this Actor

	Capability
🎯	Built for the job. Single-purpose PDF-to-JSON pipeline with sensible defaults.
🧠	AI-driven extraction. Adapts to layout variation without code changes.
⚙️	Configurable. Specify fields or pass a custom prompt for domain-specific extraction.
🔁	Live processing. Every run runs end to end with no caching of input PDFs.
🌐	No infra to manage. Apify handles compute, scaling, scheduling, and storage.
🛡️	Reliable. Per-file error reporting means one bad PDF does not kill the whole run.
🚫	No code required. Configure in the UI, run from CLI, schedule via cron, or call from any language with the Apify SDK.

📊 Production-grade PDF parsing without writing or maintaining custom parsers per document type.

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📈 How it compares to alternatives

Approach	Cost	Coverage	Refresh	Accuracy	Setup
⭐ PDF to JSON Parser (this Actor)	$5 free credit, then pay-per-use	Any PDF	Live per run	High, layout-agnostic	⚡ 2 min
Hand-written parsers	Engineering hours	Per layout	Whenever you maintain it	High but brittle	🐢 Days to weeks
OCR-only tools	$$ monthly	Text extraction only	Live	Medium	⏳ Hours
Manual data entry	Hours per file	Limited	Stale	Variable	🕒 Variable

Pick this Actor when you want flexible, layout-agnostic PDF parsing without owning the infrastructure.

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🚀 How to use

1. 📝 Sign up. Create a free account with $5 credit (takes 2 minutes).
2. 🌐 Open the Actor. Go to the PDF to JSON Parser page on the Apify Store.
3. 🎯 Upload your PDFs. Drop one or more PDFs and (optionally) list the fields you need.
4. 🚀 Run it. Click Start and let the Actor extract structured data.
5. 📥 Download. Grab your results in the Dataset tab as CSV, Excel, JSON, or XML.

⏱️ Total time from signup to first parsed PDF: 3-5 minutes for a short document.

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💼 Business use cases

📊 Finance and AP automation Auto-extract invoice data into accounting systems Parse expense reports for reimbursement workflows Pull line items from vendor PDFs for analysis Build searchable archives of financial documents	🏢 Legal and contract ops Extract parties, dates, and key clauses from contracts Build searchable contract repositories Surface auto-renewal triggers and termination dates Power contract intelligence and review workflows
🎯 Research and compliance Index research papers for full-text search Convert regulatory filings into queryable records Build literature databases for systematic review Power KYC and due-diligence workflows from filings	🛠️ Engineering and product Replace bespoke PDF parsers across products Add document intelligence to SaaS tools Wire datasets into your apps via the Apify API or webhooks Skip the layout-handling and OCR maintenance entirely

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🌟 Beyond business use cases

Data like this powers more than commercial workflows. The same structured records support research, education, civic projects, and personal initiatives.

🎓 Research and academia Empirical datasets for papers, thesis work, and coursework Longitudinal studies tracking changes across snapshots Reproducible research with cited, versioned data pulls Classroom exercises on data analysis and ethical scraping	🎨 Personal and creative Side projects, portfolio demos, and indie app launches Data visualizations, dashboards, and infographics Content research for bloggers, YouTubers, and podcasters Hobbyist collections and personal trackers
🤝 Non-profit and civic Transparency reporting and accountability projects Advocacy campaigns backed by public-interest data Community-run databases for local issues Investigative journalism on public records	🧪 Experimentation Prototype AI and machine-learning pipelines with real data Validate product-market hypotheses before engineering spend Train small domain-specific models on niche corpora Test dashboard concepts with live input

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🔌 Automating PDF to JSON Parser

This Actor exposes a REST endpoint, so you can drive it from any language or workflow tool.

• Node.js - call it via the Apify JS SDK.
• Python - call it via the Apify Python SDK.
• REST - hit it directly through the Apify v2 API.

Schedules. Use Apify Scheduler to process a folder of PDFs on a cron cadence. Combine with webhooks to trigger downstream workflows the moment parsing completes.

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❓ Frequently Asked Questions

🔌 Integrate with any app

PDF to JSON Parser connects to any cloud service via Apify integrations:

• Make - Automate multi-step workflows
• Zapier - Connect with 5,000+ apps
• Slack - Get run notifications in your channels
• Airbyte - Pipe results into your warehouse
• GitHub - Trigger runs from commits and releases
• Google Drive - Export datasets straight to Sheets

You can also use webhooks to trigger downstream actions when a parse completes, like firing a summarization actor or pinging a Slack channel.

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🔗 Recommended Actors

• 📰 Article Extractor - Extract clean article text from any URL
• 🎤 Audio Transcriber - Convert audio recordings to structured text
• 📊 HTML to JSON Smart Parser - Parse any HTML page into structured JSON
• 🎬 YouTube AI Transcriber - Transcribe YouTube videos via URL
• 🌐 Website Content Crawler - Crawl entire sites and export structured content

💡 Pro Tip: browse the complete ParseForge collection for more reference-data scrapers.

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🆘 Need Help? Open our contact form to request a new actor, propose a custom project, or report an issue.

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⚠️ Disclaimer. This Actor is an independent tool. The actor processes only PDFs you supply by URL and is intended for legitimate document automation workflows. Users are responsible for ensuring they hold the rights to parse the PDFs they submit and for compliance with copyright, privacy, and licensing laws in their jurisdiction.