Python 3 Practical Projects: PDF/Word Conversion, Image Processing, and OCR Tools

The article introduces seven practical Python3 projects that automate common file‑format conversions and image processing tasks, providing step‑by‑step instructions and full code snippets for each tool.

1. PDF to Word

Using pdfplumber to extract text from a PDF and python-docx to create a Word document.

import pdfplumber
import docx

def pdf_to_word(pdf_path, word_path):
    with pdfplumber.open(pdf_path) as pdf:
        text = ""
        for page in pdf.pages:
            text += page.extract_text()
    doc = docx.Document()
    doc.add_paragraph(text)
    doc.save(word_path)

2. Image to PDF

Utilizing the Pillow library to combine multiple images of equal size into a single PDF file.

from PIL import Image

def image_to_pdf(image_paths, pdf_path):
    images = []
    for image_path in image_paths:
        image = Image.open(image_path)
        images.append(image)
    images[0].save(pdf_path, save_all=True, append_images=images[1:])

3. Image to Word

Inserting images into a Word document with python-docx and Pillow.

import docx
from PIL import Image

def image_to_word(image_path, word_path):
    doc = docx.Document()
    doc.add_picture(image_path)
    doc.save(word_path)

4. Image Compression

Compressing images by resizing and saving with Pillow’s optimization options.

from PIL import Image

def compress_image(image_path, compressed_path, quality=50):
    image = Image.open(image_path)
    image.save(compressed_path, optimize=True, quality=quality)

5. Image Filtering (Gaussian Blur)

Applying Gaussian blur for noise reduction while preserving edges using OpenCV.

import cv2

def gaussian_blur(image_path, result_path):
    img = cv2.imread(image_path)
    blur = cv2.GaussianBlur(img, (5, 5), 0)
    cv2.imwrite(result_path, blur)

6. Image to Excel

Converting image pixel data into an Excel spreadsheet with Pillow and pandas.

import pandas as pd
from PIL import Image

def image_to_excel(image_paths, excel_path):
    df = pd.DataFrame()
    for i in range(len(image_paths)):
        image = Image.open(image_paths[i])
        data = list(image.getdata())
        width, height = image.size
        arr = pd.DataFrame(data, columns=['R', 'G', 'B'])
        arr['row'] = [i + 1] * height
        arr['col'] = list(range(1, width + 1)) * height
        arr.set_index(['row', 'col'], inplace=True)
        df = df.append(arr)
    df = df.unstack(level=0)
    df.columns = df.columns.droplevel()
    df.index.name = None
    df.columns.name = None
    df.to_excel(excel_path)

7. Text Recognition (OCR)

Extracting text from images using Pillow and pytesseract.

import pytesseract
from PIL import Image

def recognize_text(image_path):
    image = Image.open(image_path)
    text = pytesseract.image_to_string(image)
    return text

These seven tools demonstrate how Python can efficiently handle everyday document and image processing needs.