Extract Text from PDFs with Python 📄

In this tutorial, you'll learn **how to extract text from PDF files using Python** — a must-have skill for anyone working with documents, data scraping, or automating workflows involving PDFs.

PDFs are everywhere — invoices, reports, articles, books — and being able to programmatically pull text from them opens the door to **searching**, **indexing**, **summarizing**, or even converting PDFs to other formats (like CSV or TXT). Whether you're a data analyst, developer, or automator, this guide will get you started with ease.

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### ✅ What You'll Learn:

🔹 How to install the required libraries for PDF reading
🔹 How to extract text from simple and complex PDFs
🔹 Difference between text-based and scanned/image-based PDFs
🔹 Handling multi-page PDFs and extracting specific pages
🔹 Tips to clean and process extracted text

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### 🔧 Tools & Libraries Covered:

- [`PyPDF2`](https://pypi.org/project/PyPDF2/) – lightweight, pure Python library for reading PDFs
- [`pdfplumber`](https://pypi.org/project/pdfplumber/) – best for accurate text layout extraction
- [`PyMuPDF` / `fitz`](https://pypi.org/project/PyMuPDF/) – fast and powerful, handles both text and images
- [`Tesseract`](https://github.com/tesseract-ocr/tesseract) – for OCR if your PDF is scanned

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### 🧪 Sample Workflow:

```python
# Using PyPDF2
import PyPDF2

with open("example.pdf", "rb") as file:
reader = PyPDF2.PdfReader(file)
for page in reader.pages:
print(page.extract_text())
```

```python
# Using pdfplumber for better layout
import pdfplumber

with pdfplumber.open("example.pdf") as pdf:
for page in pdf.pages:
print(page.extract_text())
```

```python
# OCR with pytesseract for scanned PDFs
from PIL import Image
import pytesseract
import fitz # PyMuPDF

doc = fitz.open("scanned.pdf")
for page_num in range(len(doc)):
pix = doc.load_page(page_num).get_pixmap()
img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
text = pytesseract.image_to_string(img)
print(text)
```

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### 💡 Pro Tips:

- Use `pdfplumber` for tabular data and layout-sensitive content.
- Use `PyMuPDF` (fitz) if you need images or metadata too.
- For scanned/image PDFs, OCR with Tesseract is a must.
- Always clean extracted text using `.strip()`, regex, or `re.sub()` for better results.

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✨ If this video helps you extract valuable insights from PDFs, give it a **thumbs up**, **subscribe**, and drop your questions in the comments!

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