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📄 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.

📋 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.

🎬 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.

⚙️ Input

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.

📊 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

📦 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"
}

✨ Why choose this Actor

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

📈 How it compares to alternatives

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

🚀 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.

💼 Business use cases

🌟 Beyond business use cases

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

🔌 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.

❓ 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.

🔗 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.

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

⚠️ 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.