Best PDF Extraction Tools in 2026

Real-World Use Cases

• •Chunking legal contracts into semantically meaningful sections for RAG retrieval
• •Extracting tables from financial reports into structured JSON for downstream analysis
• •Batch processing thousands of mixed-format documents (PDF, DOCX, PPTX) into a unified schema
• •Building knowledge bases from research papers with preserved section hierarchy

Integration Example

from unstructured.partition.pdf import partition_pdf

elements = partition_pdf(
    filename="contract.pdf",
    strategy="hi_res",
    infer_table_structure=True,
    extract_images_in_pdf=True
)

for element in elements:
    print(f"{element.category}: {element.text[:100]}")
    if element.category == "Table":
        print(element.metadata.text_as_html)

Real-World Use Cases

• •Converting complex research papers with equations and figures into clean markdown for LLM consumption
• •Extracting structured data from scanned invoices with irregular layouts
• •Building LlamaIndex-based Q&A systems over large document collections
• •Parsing government forms and compliance documents with mixed table and text content

Integration Example

from llama_parse import LlamaParse
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

parser = LlamaParse(
    api_key="llx-...",
    result_type="markdown",
    num_workers=4
)

documents = SimpleDirectoryReader(
    input_files=["report.pdf"],
    file_extractor={".pdf": parser}
).load_data()

index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What were Q4 revenues?")

Real-World Use Cases

• •Building enterprise search indexes from heterogeneous document repositories
• •Extracting metadata (author, dates, keywords) from thousands of PDFs for cataloging
• •Processing mixed document archives where format coverage matters more than layout fidelity
• •Integrating document extraction into Java-based enterprise middleware stacks

Integration Example

from tika import parser

# Extract text and metadata from a PDF
parsed = parser.from_file("document.pdf")

text = parsed["content"]
metadata = parsed["metadata"]

print(f"Title: {metadata.get('title', 'N/A')}")
print(f"Author: {metadata.get('Author', 'N/A')}")
print(f"Pages: {metadata.get('xmpTPg:NPages', 'N/A')}")
print(f"Content preview: {text[:500]}")

Real-World Use Cases

• •Converting academic papers into structured JSON with preserved section hierarchy and references
• •Extracting complex tables from scientific publications for data mining
• •Building open-source document processing pipelines without vendor dependencies
• •Processing patent documents with mixed diagrams, tables, and dense text

Integration Example

from docling.document_converter import DocumentConverter

converter = DocumentConverter()
result = converter.convert("research_paper.pdf")

# Access structured document
doc = result.document
print(f"Title: {doc.title}")

# Export as markdown
markdown = result.document.export_to_markdown()

# Export as structured JSON
json_output = result.document.export_to_dict()
for table in doc.tables:
    print(table.export_to_dataframe())

Real-World Use Cases

• •Extracting financial tables from annual reports with multi-page spanning rows
• •Parsing medical records with mixed handwriting, stamps, and printed text
• •Converting scanned construction blueprints with embedded specification tables
• •Processing insurance claim documents with nested form structures

Integration Example

from reducto import Reducto

client = Reducto(api_key="r_...")

result = client.parse(
    file="annual_report.pdf",
    options={
        "table_mode": "accurate",
        "return_bounding_boxes": True
    }
)

for chunk in result.chunks:
    print(f"Type: {chunk.type}, Content: {chunk.content[:100]}")
    if chunk.type == "table":
        print(chunk.to_dataframe())

Real-World Use Cases

• •Batch converting academic paper archives into markdown for RAG knowledge bases
• •Extracting textbook content with equations and code blocks into readable markdown
• •Processing multi-column conference proceedings into single-column readable format
• •Converting scanned book pages into searchable, clean markdown text

Integration Example

from marker.converters.pdf import PdfConverter
from marker.models import create_model_dict

models = create_model_dict()
converter = PdfConverter(artifact_dict=models)

rendered = converter("paper.pdf")

# Access markdown output
markdown_text = rendered.markdown
print(markdown_text[:500])

# Access extracted images
for img in rendered.images:
    img.save(f"extracted_{img.id}.png")

Real-World Use Cases

• •High-speed text extraction from millions of machine-generated PDF invoices
• •Extracting and cataloging all images embedded in large PDF document sets
• •Building PDF preprocessing pipelines that feed into downstream ML models
• •Redacting sensitive information from PDF documents programmatically

Integration Example

import fitz  # PyMuPDF

doc = fitz.open("document.pdf")

for page_num, page in enumerate(doc):
    # Extract text with layout preservation
    text = page.get_text("text")

    # Extract text blocks with position info
    blocks = page.get_text("dict")["blocks"]
    for block in blocks:
        if block["type"] == 0:  # text block
            for line in block["lines"]:
                print(line["spans"][0]["text"])

    # Extract images
    for img in page.get_images():
        xref = img[0]
        pix = fitz.Pixmap(doc, xref)
        pix.save(f"page{page_num}_img{xref}.png")

Real-World Use Cases

• •Automating data entry from scanned paper forms and applications
• •Extracting key-value pairs from government IDs and driver's licenses
• •Processing handwritten medical records into structured electronic health records
• •Building document automation workflows with Lambda triggers on S3 uploads

Integration Example

import boto3

textract = boto3.client("textract")

response = textract.analyze_document(
    Document={"S3Object": {"Bucket": "docs", "Name": "form.pdf"}},
    FeatureTypes=["TABLES", "FORMS"]
)

for block in response["Blocks"]:
    if block["BlockType"] == "LINE":
        print(f"Text: {block['Text']}")
    elif block["BlockType"] == "TABLE":
        print(f"Table at confidence: {block['Confidence']:.1f}%")
    elif block["BlockType"] == "KEY_VALUE_SET":
        print(f"Form field detected")

Real-World Use Cases

• •Building browser-based document processing tools that extract and display PDF content
• •Creating Node.js microservices for text extraction from simple native PDFs
• •Implementing custom text extraction logic for domain-specific PDF formats
• •Rendering PDF pages as images for downstream vision model processing

Integration Example

const pdfjsLib = require("pdfjs-dist/legacy/build/pdf.js");

async function extractText(pdfPath) {
  const doc = await pdfjsLib.getDocument(pdfPath).promise;
  const results = [];

  for (let i = 1; i <= doc.numPages; i++) {
    const page = await doc.getPage(i);
    const content = await page.getTextContent();
    const text = content.items.map(item => item.str).join(" ");
    results.push({ page: i, text });
  }

  return results;
}

extractText("report.pdf").then(pages =>
  pages.forEach(p => console.log('Page ${p.page}: ${p.text.slice(0, 100)}'))
);