PDF Parser & Table Extractor is a paid API for AI agents from visual.hugen.tokyo, paid per call via x402, $0.02/call, not verified (last probed 2026-10-30); live alternatives: agentsvc.io PDF Text Extraction ($0.0055) and netintel.dev Web Extract ($0.003).
Agents call it through Zero, which pays per call: no API key and no wallet of your own. New accounts start with $5. Live alternatives for the same use case: agentsvc.io PDF Text Extraction ($0.0055) · netintel.dev Web Extract ($0.003).
Extracts structured text, tables as JSON arrays, and metadata from any PDF URL on a pay-per-use basis
Extract structured text, tables, and metadata from any PDF URL. Returns page-by-page text content, detected tables as JSON arrays, and document metadata (title, author, page count). No PDF library or OCR setup needed — pay per extraction. AI agent API for document intelligence and data extraction
Returns page-by-page text content, all detected tables as JSON arrays (rows and columns), and document metadata including title, author, and total page count — all structured and ready to use programmatically.
GEThttps://visual.hugen.tokyo/visual/parse?utm_source=zero.xyzUse this endpoint when you need to extract structured content from a PDF hosted at a public URL without setting up any local PDF libraries, OCR infrastructure, or browser automation. Ideal for document intelligence pipelines, data extraction from reports, invoices, research papers, or any PDF with tabular data.
{
"url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf",
"max_pages": 1
}| Field | Type | Description |
|---|---|---|
| input | — |
{
"pages": [
{
"page": 1,
"text": "Dummy PDF file",
"char_count": 14
}
],
"metadata": {
"title": "",
"author": "Evangelos Vlachogiannis",
"creator": "Writer",
"subject": "",
"producer": "OpenOffice.org 2.1",
"page_count": 1,
"file_size_bytes": 13264,
"pages_extracted": 1
},
"extracted_at": "2026-05-29T22:39:26Z",
"summary_stats": {
"has_text": true,
"total_tables": 0,
"total_characters": 14
}
}{
"type": "json",
"example": {
"metadata": {
"title": "Q1 2026 Financial Report",
"page_count": 12,
"file_size_bytes": 284672
},
"summary_stats": {
"has_text": true,
"total_tables": 3,
"total_characters": 18420
}
}
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