Top 7 Data Extraction from PDF Tools

PDFs hold a massive amount of the world's structured data — financial reports, research papers, invoices, legal contracts. But extracting that data programmatically is harder than it should be. Fonts change, layouts vary, scanned PDFs need OCR, and tables rarely convert cleanly.

This guide compares the top 7 tools for extracting data from PDFs, covering accuracy, pricing, and developer experience.

Key Takeaways

• Python-native tools like pdfplumber and PyMuPDF offer the best accuracy for structured PDFs
• Cloud APIs (Google Document AI, AWS Textract) handle scanned documents and handwriting
• LLM-based extraction (SearchHive DeepDive) excels at understanding complex layouts
• Choosing the right tool depends on PDF complexity, volume, and budget

The 7 Best PDF Data Extraction Tools

1. pdfplumber

Best for: Structured PDFs with tables and forms. Python-native, no API calls needed.

pdfplumber is a Python library that extracts text, tables, and metadata from PDF files. It handles most structured documents well, including multi-page tables with merged cells.

import pdfplumber

def extract_tables(pdf_path):
    with pdfplumber.open(pdf_path) as pdf:
        all_tables = []
        for page in pdf.pages:
            tables = page.extract_tables()
            for table in tables:
                all_tables.append(table)
        return all_tables

tables = extract_tables("financial_report.pdf")
for row in tables[0]:
    print(row)

• Cost: Free (MIT license)
• Accuracy: High for text-based PDFs, no OCR support
• Volume: Unlimited (local processing)
• Limitations: Struggles with scanned PDFs, complex layouts, and handwriting

2. PyMuPDF (fitz)

Best for: Fast text extraction and image extraction from PDFs.

PyMuPDF (imported as fitz) is a Python binding for MuPDF. It's fast, well-maintained, and handles both text and image extraction.

import fitz  # PyMuPDF

def extract_text_and_images(pdf_path):
    doc = fitz.open(pdf_path)
    text = ""
    images = []
    for page_num, page in enumerate(doc):
        text += page.get_text()
        for img in page.get_images(full=True):
            images.append(img)
    doc.close()
    return text, images

text, images = extract_text_and_images("report.pdf")

• Cost: Free for open-source use (AGPL), commercial license available
• Accuracy: High for text, excellent for images
• Speed: Very fast — one of the fastest Python PDF libraries
• Limitations: No built-in OCR, table extraction is manual

3. Google Cloud Document AI

Best for: Scanned documents, handwriting, and enterprise-scale processing.

Google's Document AI uses machine learning to extract text, forms, and tables from documents including scanned PDFs and images. It handles handwriting, multiple languages, and complex layouts.

from google.cloud import documentai_v1 as documentai

def extract_with_document_ai(file_path, project_id, location="us"):
    client = documentai.DocumentProcessorServiceClient()
    # Process document
    with open(file_path, "rb") as f:
        document_content = f.read()
    # Requires GCP project setup and processor configuration
    # Returns structured JSON with text, forms, and tables

• Cost: $1.50 per 1,000 pages (first 1M), then $0.60/1K
• Accuracy: Very high, especially for scanned documents
• Volume: Scales to millions of pages
• Limitations: Requires GCP setup, latency for API calls, costs add up at scale

4. AWS Textract

Best for: AWS users needing forms and tables from documents.

AWS Textract extracts text, handwriting, and table data from scanned documents. It integrates well with other AWS services.

import boto3

def extract_with_textract(file_path):
    client = boto3.client("textract")
    with open(file_path, "rb") as f:
        response = client.detect_document_text(Document={"Bytes": f.read()})
    blocks = response["Blocks"]
    text = "\n".join(b["Text"] for b in blocks if b["BlockType"] == "LINE")
    return text

• Cost: $1.50/1K pages (first 1M), $0.60/1K after
• Accuracy: High for forms and tables, decent handwriting
• Volume: Scales well within AWS ecosystem
• Limitations: AWS dependency, no Python-native processing option

5. SearchHive DeepDive (PDF via Web)

Best for: Extracting data from PDFs available online, combining extraction with research.

SearchHive's DeepDive API can fetch and analyze PDFs from URLs, extracting structured data and answering questions about the content. When combined with ScrapeForge, it handles PDFs that require JavaScript to load.

import httpx

def extract_pdf_content(url: str) -> str:
    """Fetch a PDF from a URL and extract its content."""
    resp = httpx.post(
        "https://api.searchhive.dev/v1/scrapeforge",
        json={"url": url, "format": "markdown"},
        headers={"Authorization": "Bearer YOUR_KEY"}
    )
    data = resp.json()
    return data.get("content", "")

# Extract from a publicly hosted PDF
content = extract_pdf_content("https://example.com/annual-report.pdf")

For complex extraction tasks, DeepDive can process PDF content and answer specific questions:

def analyze_pdf(query: str, context_url: str) -> str:
    """Research a topic based on a PDF document."""
    resp = httpx.post(
        "https://api.searchhive.dev/v1/deepdive",
        json={"query": query, "source_urls": [context_url], "depth": 2},
        headers={"Authorization": "Bearer YOUR_KEY"}
    )
    return resp.json().get("answer", "")

• Cost: Credits-based (500 free, $9/mo for 5K)
• Accuracy: High for online PDFs, leverages LLM understanding
• Volume: Flexible — credit-based scaling
• Limitations: Best for web-accessible PDFs, not bulk local file processing

6. Camelot

Best for: Table extraction specifically — when tables are your primary target.

Camelot is a Python library designed exclusively for extracting tables from PDFs. It uses two strategies: stream (for bordered tables) and lattice (for bordered and borderless tables).

import camelot

tables = camelot.read_pdf("financial_report.pdf", pages="all", flavor="lattice")
for table in tables:
    print(table.df)  # Returns a pandas DataFrame
    table.to_csv(f"table_{table.page}.csv")

• Cost: Free (MIT license for core, LGPL for PDF processing)
• Accuracy: Excellent for well-structured tables
• Volume: Unlimited (local processing)
• Limitations: Only handles tables, not general text extraction

7. Unstructured

Best for: Pre-processing documents for LLM pipelines and RAG systems.

Unstructured is an open-source library that partitions documents into structured elements (titles, headers, lists, tables, narrative text). It's designed for preparing documents for vector databases and LLM input.

from unstructured.partition.auto import partition

elements = partition(filename="report.pdf")
for element in elements:
    print(f"{element.type}: {element.text[:100]}")

• Cost: Open-source (Apache 2.0), hosted API available
• Accuracy: Good for document structure, depends on PDF complexity
• Volume: Local processing unlimited, hosted API has limits
• Limitations: Can be slow on large documents, complex setup for advanced features

Comparison Table

How to Choose

Your PDFs are local, text-based, and have tables: Start with pdfplumber. Free, accurate, Python-native. Add Camelot if tables need extra precision.

Your PDFs are scanned or contain handwriting: Use Google Document AI or AWS Textract. Both handle OCR well. Pick based on your cloud provider preference.

Your PDFs are online and you need context: SearchHive's ScrapeForge extracts PDF content from URLs, and DeepDive can answer questions about the content. Ideal for research workflows.

You're building a RAG pipeline: Unstructured partitions documents into semantic elements perfect for chunking and embedding.

You need maximum speed: PyMuPDF is the fastest option for raw text extraction from text-based PDFs.

Recommendation

For most developers, start with pdfplumber for local PDF processing and SearchHive for web-hosted PDFs. The combination covers both scenarios without enterprise cloud API costs. If you hit accuracy limits with complex layouts, graduate to Google Document AI or AWS Textract for OCR capability.

Get Started with SearchHive

Need to extract data from PDFs hosted online? SearchHive's ScrapeForge API handles PDF content extraction from URLs with no setup required. Get 500 free credits to test it out.

• Get your free API key
• API documentation
• See also: /blog/how-to-data-extraction-pipeline-step-by-step