Best Data Extraction from PDF Tools in 2025: Complete Comparison

PDFs remain one of the most stubborn data formats. Despite the web's shift to HTML and free JSON formatter, businesses still run on PDFs -- invoices, contracts, research papers, government filings, financial reports. Extracting structured data from PDFs programmatically is a problem every data team eventually faces.

This guide compares the best PDF data extraction tools available in 2025, from open-source Python libraries to enterprise APIs, so you can pick the right tool for your use case.

Key Takeaways

• Python libraries (PyMuPDF, pdfplumber) are best for simple, high-volume extraction at zero cost
• Cloud APIs (Google Document AI, AWS Textract) handle complex layouts and handwriting but are expensive
• AI-powered tools (SearchHive DeepDive, LLM-based extractors) excel at unstructured PDFs with varied formats
• The right choice depends on PDF complexity, volume, budget, and whether you need structured output or raw text
• SearchHive's credit system lets you combine PDF extraction with web search and scraping in one workflow

Top PDF Data Extraction Tools Compared

1. PyMuPDF (fitz) -- Fast PDF Text Extraction

PyMuPDF is the fastest pure-Python PDF library for text extraction. It handles text, images, and metadata with minimal dependencies.

import fitz  # PyMuPDF

doc = fitz.open("report.pdf")
for page_num, page in enumerate(doc):
    text = page.get_text()
    print(f"Page {page_num + 1}: {text[:200]}...")
doc.close()

Pros: Blazing fast (C core), minimal dependencies, handles encrypted PDFs, active development Cons: No table extraction, no OCR, limited layout analysis Best for: High-volume text extraction from well-structured PDFs Pricing: Free (AGPL), commercial license available

2. pdfplumber -- Table Extraction Champion

pdfplumber excels at extracting tables from PDFs, which is where most PDF libraries fall short.

import pdfplumber

with pdfplumber.open("financials.pdf") as pdf:
    page = pdf.pages[0]
    tables = page.extract_tables()
    for table in tables:
        for row in table:
            print(row)
    
    # Extract text with position data
    text = page.extract_text()
    words = page.extract_words()

Pros: Excellent table detection, character-level positioning, integrates with pandas Cons: Slower than PyMuPDF, struggles with merged cells, no OCR Best for: Financial reports, invoices, any PDF with tables Pricing: Free (MIT license)

3. Google Cloud Document AI -- Enterprise-Grade Extraction

Google's Document AI uses machine learning to understand document structure, including forms, tables, and handwritten text.

from google.cloud import documentai_v1 as documentai

project_id = "your-project"
location = "us"
processor_id = "your-processor-id"

client = documentai.DocumentProcessorServiceClient()
name = f"projects/{project_id}/locations/{location}/processors/{processor_id}"

with open("invoice.pdf", "rb") as f:
    document = client.process_document(
        request=documentai.ProcessRequest(name=name, raw_document={"content": f.read(), "mime_type": "application/pdf"})
    ).document

for entity in document.entities:
    print(f"{entity.type_}: {entity.mention_text}")

Pros: Handles handwriting, complex layouts, custom model training, 50+ pre-trained processors Cons: Expensive ($1.50/first 1000 pages), Google Cloud dependency, cold start latency Best for: Enterprise document processing at scale Pricing: First 1K pages free, then $1.50/1K pages (general), $30/1K pages (OCR)

4. AWS Textract -- Amazon's Document Analysis

AWS Textract competes directly with Google Document AI, with strong table extraction and form processing.

import boto3

textract = boto3.client("textract")

with open("document.pdf", "rb") as f:
    response = textract.analyze_document(
        Document={"Bytes": f.read()},
        FeatureTypes=["TABLES", "FORMS"]
    )

for block in response["Blocks"]:
    if block["BlockType"] == "KEY_VALUE_SET":
        print(f"Key: {block.get('Key', 'Value')}")

Pros: Strong table and form extraction, integrates with AWS ecosystem, pay-per-page Cons: AWS dependency, complex pricing tiers, inconsistent with rotated text Best for: Teams already on AWS Pricing: $1.50/1K pages (standard), $5/1K pages (with tables/forms)

5. LlamaParse -- LLM-Powered PDF Understanding

LlamaParse (from LlamaIndex) uses LLMs to understand PDF structure and extract semantically meaningful content.

from llama_parse import LlamaParse

parser = LlamaParse(api_key="llx-xxxxx", result_type="markdown")
documents = parser.load_data("complex_report.pdf")

for doc in documents:
    print(doc.text[:500])

Pros: Understands complex layouts, outputs markdown, good for RAG pipelines Cons: Slower than rule-based tools, depends on LLM quality, pricing scales with complexity Best for: RAG pipelines and LLM applications Pricing: Free tier (7K pages/day), paid plans from $0.003/page

6. Marker -- Open-Source PDF to Markdown

Marker converts PDFs to high-quality markdown using a combination of deep learning models for layout detection and OCR.

# Install: pip install marker-pdf
# CLI usage:
# marker_single input.pdf output_dir --output_format markdown

from marker.converters.pdf import PdfConverter
from marker.renderers.markdown import MarkdownRenderer

converter = PdfConverter(artifacts_dict=MarkdownRenderer())
rendered = converter("document.pdf")
print(rendered.markdown[:500])

Pros: Free and open source, excellent markdown output, handles multi-column layouts Cons: GPU recommended, slower than pure-Python tools, large dependency footprint Best for: Converting PDFs to markdown for LLM consumption Pricing: Free (GPL-3.0)

7. SearchHive DeepDive -- PDF Research and Extraction

SearchHive's DeepDive API combines web search with document understanding, making it useful for extracting data from PDFs that are publicly accessible online.

import httpx

resp = httpx.post(
    "https://api.searchhive.dev/v1/deepdive",
    json={
        "query": "extract key financial data from Company X 2024 annual report PDF",
        "depth": "detailed",
        "include_sources": True
    },
    headers={"Authorization": "Bearer sh_live_xxxxx"}
)

research = resp.json()
print(research["summary"])
# Sources include PDF URLs with extracted data points

SearchHive doesn't upload your PDFs -- it finds and extracts data from publicly available documents on the web. For extracting data from your own PDFs, combine it with PyMuPDF or pdfplumber, then use SwiftSearch to enrich the extracted data with web context.

Best for: Research workflows that need to find and extract data from public PDFs Pricing: 500 free credits, then $0.0001/credit

Comparison Table

Recommendation

• For free, high-volume text extraction: PyMuPDF
• For table extraction: pdfplumber
• For enterprise document processing: Google Document AI or AWS Textract
• For LLM/RAG pipelines: LlamaParse or Marker
• For research and web-based PDF data: SearchHive DeepDive

Most teams benefit from a combination. Use PyMuPDF or pdfplumber for your own PDFs, and SearchHive for discovering and extracting data from public documents on the web. The credit system means you're not paying separate bills for each capability.

Get Started

Try SearchHive free with 500 credits -- combine web search, scraping, and AI research in one API. No credit card required. /blog/complete-guide-to-api-for-llm-integration /blog/top-7-python-sdk-design-tools