364 Local Tools 35383463

Local PDF Extraction Tools

PyMuPDF (fitz) + pymupdf4llm

Best for: Fast extraction, LLM-optimized output, complex layouts.

Speed: Fastest option (~0.01s per page)

Installation

pip install pymupdf4llm
# or with uv
uv pip install pymupdf4llm

Usage

import pymupdf4llm

# Basic extraction to markdown
md_text = pymupdf4llm.to_markdown("document.pdf")

# With options
md_text = pymupdf4llm.to_markdown(
    "document.pdf",
    pages=[0, 1, 2],  # specific pages (0-indexed)
    page_chunks=True,  # return list of page chunks for RAG
)

# For RAG systems - get chunks with metadata
chunks = pymupdf4llm.to_markdown("document.pdf", page_chunks=True)
for chunk in chunks:
    print(f"Page {chunk['metadata']['page']}: {chunk['text'][:100]}...")

Strengths

• Fastest Python PDF library
• Good structure preservation (headings, lists)
• LLM-optimized markdown output
• Handles images and complex layouts
• Active development

Limitations

• Struggles with some scanned documents
• Table extraction less reliable than pdfplumber

pdfplumber

Best for: Table extraction, machine-generated PDFs, detailed layout control.

Speed: Moderate (~0.1s per page)

Installation

pip install pdfplumber

Usage

import pdfplumber

with pdfplumber.open("document.pdf") as pdf:
    for page in pdf.pages:
        # Extract text
        text = page.extract_text()
        
        # Extract tables
        tables = page.extract_tables()
        for table in tables:
            for row in table:
                print(row)
        
        # Visual debugging
        im = page.to_image()
        im.draw_rects(page.chars)
        im.save("debug.png")

Table Extraction Options

# Custom table settings for complex tables
table_settings = {
    "vertical_strategy": "text",
    "horizontal_strategy": "text",
    "snap_tolerance": 3,
    "join_tolerance": 3,
}

tables = page.extract_tables(table_settings)

Strengths

• Excellent table detection and extraction
• Fine-grained control over extraction
• Visual debugging tools
• Good for structured documents

Limitations

• Slower than PyMuPDF
• Does not work well on scanned PDFs
• Built on pdfminer.six (can be memory-heavy)

unstructured

Best for: RAG systems, semantic chunking, multi-format support.

Speed: Slower (includes ML models)

Installation

pip install unstructured[pdf]
# For full features including OCR:
pip install unstructured[all-docs]

Usage

from unstructured.partition.pdf import partition_pdf

# Basic extraction
elements = partition_pdf("document.pdf")

for element in elements:
    print(f"{element.category}: {element.text[:100]}")

# With OCR for scanned documents
elements = partition_pdf(
    "scanned.pdf",
    strategy="ocr_only",  # or "hi_res" for best quality
)

# Chunking for RAG
from unstructured.chunking.title import chunk_by_title
chunks = chunk_by_title(elements)

Strengths

• Semantic element detection (titles, paragraphs, tables)
• Built-in chunking strategies for RAG
• Multi-format support (PDF, DOCX, HTML, etc.)
• OCR support included

Limitations

• Heavier dependencies
• Slower processing
• More complex setup

pytesseract + pdf2image

Best for: Scanned PDFs when API access unavailable.

Speed: Slow (~1-3s per page)

Prerequisites

# macOS
brew install tesseract poppler

# Ubuntu
apt-get install tesseract-ocr poppler-utils

Installation

pip install pytesseract pdf2image Pillow

Usage

import pytesseract
from pdf2image import convert_from_path

# Convert PDF to images
images = convert_from_path("scanned.pdf", dpi=300)

# OCR each page
for i, image in enumerate(images):
    text = pytesseract.image_to_string(image, lang="eng")
    print(f"Page {i+1}:\n{text}")

# Multiple languages
text = pytesseract.image_to_string(image, lang="eng+fra+deu")

Strengths

• Free and open source
• Works offline
• Supports 100+ languages

Limitations

• Requires system dependencies (Tesseract, Poppler)
• Lower accuracy than cloud OCR services
• No structure preservation

Comparison Summary

Decision Guide

1. Need speed? → pymupdf4llm
2. Need tables? → pdfplumber
3. Building RAG? → unstructured or pymupdf4llm with page_chunks
4. Scanned docs, no API? → pytesseract (or unstructured with OCR)
5. Scanned docs, have budget? → Mistral OCR API (see api-services.md)

Additional Tools Worth Knowing

marker-pdf (Datalab)

Best for: End-to-end PDF to Markdown conversion, RAG pipelines.

Built on Surya OCR engine. Very popular for document processing pipelines.

pip install marker-pdf
marker_single input.pdf --output_dir ./output --output_format markdown

Features:

• Outputs clean Markdown, JSON, or HTML
• Handles complex layouts
• Optional LLM post-processing for cleanup
• GPU-accelerated

surya

Best for: Core OCR engine, multilingual text detection.

The OCR engine that powers marker-pdf. Good for custom pipelines.

pip install surya-ocr

docling (IBM)

Best for: Enterprise document parsing, structured extraction.

pip install docling

MinerU

Best for: Academic papers, complex multi-column layouts.

Open source PDF parser with excellent layout detection.

pip install magic-pdf

PaddleOCR (Baidu)

Best for: Chinese/multilingual documents, structured forms.

pip install paddlepaddle paddleocr

Excellent for Asian languages. Requires some tuning for optimal results.

Full Comparison