PDF to JSON Converter — Free
Extract all text and metadata from any PDF into clean, structured JSON. Perfect for developers, data pipelines, and AI workflows. No signup.
MergeAny combines two or more JPG, JPEG, or PNG images into a single file directly in your browser — vertically, horizontally, or in a grid. There is no upload limit, no watermark, and your files are deleted from our servers seconds after merging. Works on Windows, Mac, iPhone, and Android with no install and no signup.
How to Convert PDF to JSON
Upload Your PDF
Select any digital PDF up to 50 MB. Scanned PDFs without a text layer will produce empty page text.
Text & Metadata Extracted
pypdf reads all text per page plus document metadata (title, author, creator, dates).
Download JSON File
Click Extract as JSON and save the structured .json file ready for any pipeline.
JSON Output Structure
The output JSON contains two top-level keys: pages and metadata. The pages array contains one object per PDF page with the page number and extracted text. The metadata object contains all available document properties.
{
"metadata": {
"title": "Annual Report 2024",
"author": "Finance Team",
"created": "2024-01-15",
"pages": 12
},
"pages": [
{ "page": 1, "text": "Executive Summary\n..." },
{ "page": 2, "text": "Revenue grew by 24%..." }
]
}This structure is immediately usable with Python's json.load(), JavaScript's JSON.parse(), or any other JSON consumer. It's also well-suited for building RAG (Retrieval-Augmented Generation) pipelines where each page becomes a chunk.
PDF to JSON Extraction Features
Structured data extraction for developers and automation workflows.
Structured JSON Output
Output includes a pages array (page number + text) and a metadata object with all document properties.
Developer-Ready
UTF-8 JSON is ready to parse with any language — Python json.load(), JavaScript JSON.parse(), or any other.
Metadata Included
Author, title, creation date, modification date, and producer fields are extracted when available.
Private Processing
Files are processed in isolated server memory and deleted immediately after download.
Page-Level Granularity
Text is split by page — perfect for building per-page search indexes or content chunking pipelines.
No Install Needed
Fully browser-based. Works on any device — Windows, Mac, or mobile.