Text Extraction • LLM-ready plain text output

PDF to TXT API: Clean Text for Search & LLMs

Extract clean, structured text from PDFs instantly. Perfect for LLM ingestion pipelines, search indexing, and data extraction. No messy formatting, no encoding errors. Just plain text ready for RAG systems and audit trails.

No credit card required • Free tier available

8,700+ teams use xspdf

Median latency: 290ms

99.95% uptime SLA

PDF to TXT API Example

REST API

curl -X POST "https://api.xspdf.com/v1/extract/text" \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input_url": "https://files.example.com/contract.pdf",
    "options": {
      "preserve_layout": false,
      "include_page_numbers": true
    }
  }'

Speed

290ms

Success

99.95%

Formats

40+

290ms

Median extraction time

99.95%

Success rate SLA

8,700+

Teams trust xspdf

UTF-8

Clean encoding

Why PDF Text Extraction Still Breaks LLM Pipelines

RAG systems need clean text. Search indexes need structured content. But PDF text extraction tools output garbled Unicode, lose paragraph breaks, and scramble table data. AI teams waste weeks debugging encoding errors and layout parsing.

Encoding Hell

Unicode errors, mangled accents, broken quotes. Text is unusable for LLMs.

Layout Chaos

Multi-column PDFs extract as jumbled sentences. Tables become gibberish.

Library Dependencies

PyPDF2, pdfplumber, PyMuPDF—all have different quirks and edge cases.

The hidden cost

AI engineering teams spend 30+ hours per quarter debugging PDF text extraction for RAG pipelines. Bad text encoding breaks vector embeddings and search relevance. One reliable API eliminates this entirely.

One API Call. Clean UTF-8 Text. LLM-Ready.

xspdf extracts clean, structured plain text from PDFs in 290ms. No encoding errors, no layout scrambling, no library dependencies. Perfect for RAG systems, search indexing, and data extraction pipelines that need reliable text.

290ms Median Extraction

Extract text from 100-page contracts in under 500ms. Batch-process thousands in parallel.

Clean UTF-8 Encoding

No Unicode errors, no mangled characters. Text is LLM-ready and search-friendly.

Layout-Aware Parsing

Multi-column PDFs, tables, and bullets extracted in reading order automatically.

Read the FAQs

Python Example

import requests

response = requests.post(
    "https://api.xspdf.com/v1/extract/text",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={
        "input_url": "https://files.example.com/contract.pdf",
        "options": {"preserve_layout": False, "include_page_numbers": True}
    }
)
text = response.json()["text"]

Built for AI and Search Pipelines

Every feature RAG systems, search engines, and data pipelines need for text extraction.

Clean UTF-8 Output

No encoding errors. Perfect for LLM ingestion and vector embeddings.

Page Number Tagging

Optional page markers for citation tracking and audit trails.

Layout Preservation

Toggle layout mode: preserve spacing or extract pure text flow.

Table Extraction

Tables converted to tab-delimited text or structured JSON.

Batch Processing

Extract text from thousands of PDFs in parallel with async webhooks.

Direct S3/GCS Storage

Output text files straight to your cloud storage bucket.

FAQ: PDF Text Extraction

Common questions about extracting clean text from PDFs

How does xspdf handle multi-column PDFs and complex layouts?

xspdf uses layout analysis to detect reading order in multi-column PDFs, newspapers, and academic papers. Text is extracted left-to-right, top-to-bottom by default. For complex layouts, enable "preserve_layout": true to maintain spatial formatting. For pure text flow (ideal for LLM ingestion), use "preserve_layout": false to strip formatting and extract linear text.

Does the API extract text from scanned PDFs (OCR)?

Yes. Enable OCR with "ocr": true in the API request. xspdf automatically detects image-based PDFs and runs optical character recognition. OCR supports 100+ languages and outputs clean UTF-8 text. For native text PDFs, OCR is skipped to maximize speed. If your PDF contains both native text and scanned images, xspdf extracts both intelligently.

Can I extract text with page numbers for citation tracking?

Yes. Set "include_page_numbers": true to inject page markers like [Page 1], [Page 2] into the text output. This is essential for RAG systems and legal workflows that require citation tracking. You can also request structured JSON output with per-page text arrays via "output_format": "json". Perfect for building search indexes with page-level granularity.

How does xspdf handle tables when extracting text?

Tables are converted to tab-delimited text by default, preserving row/column structure for parsing. For structured table data, request "output_format": "json" to get tables as arrays of objects. If your workflow requires pixel-perfect table extraction, use our dedicated PDF extraction API which returns table coordinates and cell boundaries.

How do I batch-extract text from 10,000 PDFs for a search index?

Submit extractions in parallel with async mode enabled. xspdf returns a job_id immediately, then sends a webhook to your callback URL when text is ready (typically 290ms). For large batches, use our bulk endpoint: POST an array of PDF URLs and get back a manifest of text outputs. No rate limits on enterprise plans. See API docs for LLM pipeline examples.

Still have questions? Check the full API docs.

Stop Debugging PyPDF2. Start Shipping RAG.

Join 8,700+ teams who replaced PDF text extraction libraries with one API call. No encoding errors, no layout scrambling, no library dependencies.