PDF Extraction Guide

Extract text, tables, figures, and metadata from academic PDFs using Python libraries, with strategies for handling multi-column layouts, mathematical content, and scanned documents.

PDF Extraction Tools Comparison

Tool	Text	Tables	Figures	Layout	OCR	Speed
PyMuPDF (fitz)	Excellent	Manual	Yes	Blocks	No (add with OCR engine)	Fast
pdfplumber	Good	Excellent	No	Tables focus	No	Medium
PyPDF2 / pypdf	Basic	No	No	No	No	Fast
Tabula-py	No	Excellent	No	No	No	Medium
GROBID	Structured	Yes	References	Academic layout	No	Slow (ML-based)
Nougat (Meta)	Excellent	Yes	Yes	Academic layout	Built-in	Slow (GPU)
Marker	Excellent	Yes	Yes	Multi-column	Built-in	Medium
pdf2image + Tesseract	Via OCR	Via OCR	Via OCR	No	Yes	Slow

PyMuPDF (fitz) — Fast Text Extraction

Basic Text Extraction

import fitz  # pip install PyMuPDF

def extract_text(pdf_path):
    """Extract all text from a PDF with page numbers."""
    doc = fitz.open(pdf_path)
    full_text = []

    for page_num, page in enumerate(doc, 1):
        text = page.get_text("text")
        full_text.append(f"--- Page {page_num} ---\n{text}")

    doc.close()
    return "\n".join(full_text)

# Usage
text = extract_text("paper.pdf")
print(text[:2000])

Structured Block-Level Extraction

def extract_structured(pdf_path):
    """Extract text with layout information (blocks, lines, spans)."""
    doc = fitz.open(pdf_path)
    pages = []

    for page_num, page in enumerate(doc):
        blocks = page.get_text("dict")["blocks"]
        page_data = {"page": page_num + 1, "blocks": []}

        for block in blocks:
            if "lines" not in block:
                continue  # Skip image blocks

            block_text = ""
            max_font_size = 0
            is_bold = False

            for line in block["lines"]:
                for span in line["spans"]:
                    block_text += span["text"]
                    max_font_size = max(max_font_size, span["size"])
                    if "Bold" in span.get("font", ""):
                        is_bold = True
                block_text += "\n"

            page_data["blocks"].append({
                "text": block_text.strip(),
                "font_size": max_font_size,
                "is_bold": is_bold,
                "bbox": block["bbox"]  # (x0, y0, x1, y1)
            })

        pages.append(page_data)

    doc.close()
    return pages

# Identify section headings
pages = extract_structured("paper.pdf")
for page in pages:
    for block in page["blocks"]:
        if block["is_bold"] and block["font_size"] > 11:
            print(f"[Heading] {block['text'][:80]}")

Extract Images and Figures

def extract_images(pdf_path, output_dir="./images"):
    """Extract all images from a PDF."""
    import os
    os.makedirs(output_dir, exist_ok=True)

    doc = fitz.open(pdf_path)
    img_count = 0

    for page_num, page in enumerate(doc):
        images = page.get_images(full=True)
        for img_idx, img in enumerate(images):
            xref = img[0]
            pix = fitz.Pixmap(doc, xref)

            if pix.n - pix.alpha > 3:  # CMYK
                pix = fitz.Pixmap(fitz.csRGB, pix)

            filename = f"{output_dir}/page{page_num+1}_img{img_idx+1}.png"
            pix.save(filename)
            img_count += 1

    doc.close()
    print(f"Extracted {img_count} images to {output_dir}")

pdfplumber — Table Extraction

import pdfplumber

def extract_tables(pdf_path):
    """Extract all tables from a PDF."""
    tables = []
    with pdfplumber.open(pdf_path) as pdf:
        for page_num, page in enumerate(pdf.pages):
            page_tables = page.extract_tables()
            for table_idx, table in enumerate(page_tables):
                tables.append({
                    "page": page_num + 1,
                    "table_index": table_idx,
                    "data": table
                })
    return tables

# Convert extracted table to pandas DataFrame
import pandas as pd

tables = extract_tables("paper.pdf")
for t in tables:
    if t["data"]:
        df = pd.DataFrame(t["data"][1:], columns=t["data"][0])
        print(f"\nTable on page {t['page']}:")
        print(df.to_string())

GROBID — Structured Academic Paper Parsing

GROBID uses machine learning to parse academic PDFs into structured TEI XML.

import requests

def parse_with_grobid(pdf_path, grobid_url="http://localhost:8070"):
    """Parse a paper PDF using GROBID."""
    with open(pdf_path, "rb") as f:
        response = requests.post(
            f"{grobid_url}/api/processFulltextDocument",
            files={"input": f},
            data={"consolidateHeader": 1, "consolidateCitations": 1}
        )

    if response.status_code == 200:
        return response.text  # TEI XML
    else:
        raise Exception(f"GROBID error: {response.status_code}")

# Parse the TEI XML
from lxml import etree

tei_xml = parse_with_grobid("paper.pdf")
root = etree.fromstring(tei_xml.encode())
ns = {"tei": "http://www.tei-c.org/ns/1.0"}

# Extract title
title = root.find(".//tei:titleStmt/tei:title", ns)
print(f"Title: {title.text if title is not None else 'N/A'}")

# Extract abstract
abstract = root.find(".//tei:profileDesc/tei:abstract", ns)
if abstract is not None:
    print(f"Abstract: {abstract.text}")

# Extract references
refs = root.findall(".//tei:listBibl/tei:biblStruct", ns)
print(f"References found: {len(refs)}")
for ref in refs[:5]:
    title_elem = ref.find(".//tei:title", ns)
    print(f"  - {title_elem.text if title_elem is not None else 'N/A'}")

Document Chunking for RAG

Split documents into semantically meaningful chunks for retrieval-augmented generation:

def chunk_academic_paper(pdf_path, max_chunk_size=1000, overlap=200):
    """Chunk an academic paper by sections with overlap."""
    pages = extract_structured(pdf_path)

    # Identify sections
    sections = []
    current_section = {"heading": "Preamble", "text": ""}

    for page in pages:
        for block in page["blocks"]:
            if block["is_bold"] and block["font_size"] > 11 and len(block["text"]) < 100:
                if current_section["text"].strip():
                    sections.append(current_section)
                current_section = {"heading": block["text"], "text": ""}
            else:
                current_section["text"] += block["text"] + "\n"

    if current_section["text"].strip():
        sections.append(current_section)

    # Split long sections into overlapping chunks
    chunks = []
    for section in sections:
        text = section["text"]
        if len(text) <= max_chunk_size:
            chunks.append({
                "heading": section["heading"],
                "text": text,
                "chunk_index": 0
            })
        else:
            words = text.split()
            start = 0
            chunk_idx = 0
            while start < len(words):
                end = start + max_chunk_size // 5  # Approximate words
                chunk_text = " ".join(words[start:end])
                chunks.append({
                    "heading": section["heading"],
                    "text": chunk_text,
                    "chunk_index": chunk_idx
                })
                start = end - overlap // 5  # Overlap in words
                chunk_idx += 1

    return chunks

Format Conversion

Markdown to PDF

# Using Pandoc (most versatile converter)
pandoc paper.md -o paper.pdf --pdf-engine=xelatex

# With template and bibliography
pandoc paper.md -o paper.pdf \
  --pdf-engine=xelatex \
  --template=ieee.tex \
  --bibliography=references.bib \
  --citeproc \
  --number-sections

# Markdown to Word (for collaborators who prefer Word)
pandoc paper.md -o paper.docx --reference-doc=template.docx

PDF to Markdown (Using Marker)

# Install Marker (ML-based PDF to Markdown converter)
pip install marker-pdf

# Convert a single PDF
marker_single paper.pdf output_dir/ --langs English

# Batch convert
marker output_dir/ input_dir/ --workers 4

OCR for Scanned PDFs

from pdf2image import convert_from_path
import pytesseract

def ocr_pdf(pdf_path, lang="eng"):
    """OCR a scanned PDF using Tesseract."""
    images = convert_from_path(pdf_path, dpi=300)
    full_text = []

    for i, image in enumerate(images):
        text = pytesseract.image_to_string(image, lang=lang)
        full_text.append(f"--- Page {i+1} ---\n{text}")

    return "\n".join(full_text)

# For academic papers with math, use specialized OCR:
# - Mathpix API (commercial, excellent math OCR)
# - Nougat (Meta, open source, GPU required)
# - LaTeX-OCR (open source, formula-specific)

Best Practices

1. Try PyMuPDF first: It is the fastest and handles most modern PDFs well. Fall back to GROBID for academic papers that need structural parsing.
2. Check PDF type: Use

   page.get_text()

   to detect if a PDF is text-based or scanned. If empty, use OCR.
3. Handle multi-column layouts: PyMuPDF's

   sort

   parameter in

   get_text("blocks")

   helps with reading order. GROBID and Marker handle this natively.
4. Preserve metadata: Extract DOI, authors, and title from PDF metadata (

   doc.metadata

   ) when available.
5. Validate table extraction: Always visually verify extracted tables; complex layouts with merged cells often fail.
6. Cache extracted text: Store parsed results alongside PDFs to avoid re-processing.