PDF Extraction API
Stop hand-parsing messy PDFs, retrying flaky OCR, and duct-taping regex into your ETL. Extract tables, forms, and key fields into clean structured data your models can trust—fast, consistent, and pipeline-ready. If your end goal is spreadsheets or structured payloads, jump straight to PDF to Excel or PDF to JSON.
# Extract structured data from a PDF
curl -X POST "https://api.xspdf.com/v1/pdf/extract" \
-H "Authorization: Bearer $API_KEY" \
-H "Content-Type: multipart/form-data" \
-F "file=@invoice.pdf" \
-F "output=json" \
-F "schema_hint=invoice"
# Response (truncated)
{
"document_id": "doc_8f31c2",
"pages": 2,
"fields": {
"invoice_number": "INV-10492",
"invoice_date": "2026-01-18",
"total": 1842.37,
"currency": "USD"
},
"tables": [
{ "name": "line_items", "rows": 14 }
],
"confidence": 0.94
}The challenge: unstructured PDFs quietly wreck AI pipelines
You know the feeling when your pipeline is "done"… until a single vendor changes a PDF template and suddenly your extraction returns empty strings, shifted columns, or totals that don't add up. The model trains anyway. The dashboard updates anyway. And now you're debugging data quality at 2 a.m. with a queue full of retries.
One-off rules feel fast—until your ruleset becomes the product. Every new format is a new branch, a new edge case, a new bug.
"Maybe total is on page 2." "Sometimes the table header repeats." That uncertainty poisons analytics, search, and training data.
The scariest bug isn't a 500 error—it's a "successful" response with subtly wrong numbers that ship to production.
There's a better way: OCR + layout-aware extraction you can trust
What if you could send any PDF—native, scanned, rotated, multi-column—and always get back the same clean structure? Our pdf extract api combines OCR, layout detection, and schema-friendly outputs so your AI/Data pipeline stops "interpreting" documents and starts consuming reliable data.
- 1Normalize the document
Handle scans, skew, and mixed quality inputs so you don't build a separate pre-processing pipeline just to "make OCR work."
- 2Extract structure (not just strings)
Detect tables, key-value fields, and reading order—so "Total" is the total, line items stay in columns, and pages don't drift.
- 3Ship in the format your pipeline expects
Return JSON for services, XML for enterprise workflows, or structured tabular data—without brittle post-processing.
import requests
API_KEY = "YOUR_API_KEY"
url = "https://api.xspdf.com/v1/pdf/extract"
with open("statements.pdf", "rb") as f:
r = requests.post(
url,
headers={"Authorization": f"Bearer {API_KEY}"},
files={"file": f},
data={
"output": "json",
"schema_hint": "bank_statement",
"include_tables": "true"
},
timeout=60,
)
r.raise_for_status()
data = r.json()
# Send directly to your data pipeline
print(data["fields"].keys())
print(len(data.get("tables", [])))Output formats that plug into real systems
Don't contort your pipeline around PDF weirdness. Choose the output you need and keep moving: JSON for services, XML for enterprise tooling, and table-friendly structures for analytics.
Clean keys, typed values, and structured tables—ideal for microservices, vector pipelines, and warehouses.
Explore PDF to JSON →Great when you need strict tags, legacy integration, or document-like representation with nested fields.
Extract tables that actually stay aligned—perfect for finance ops, auditing, and quick human review loops.
Explore PDF to Excel →Turn PDFs into clean data—before they cost you another sprint
Replace flaky extraction with a production pipeline you can rely on: OCR, layout detection, and schema-stable outputs. Start free in minutes—then scale when your AI, analytics, or search demands it.
- ✓ No credit card required
- ✓ JSON, XML, Excel outputs
- ✓ OCR + layout aware
- ✓ Built for data pipelines
Extract in under 5 minutes
Upload a PDF, get structured JSON. If you can call an API, you can fix your pipeline today.