How to Scrape Google Images: A Step-by-Step Guide

Welcome to a guide on how to scrape Google images. We’ll dive into the exact process of extracting image URLs, titles, and source links from Google Images search results. By the end of this guide, you'll be able to get all the image data from multiple search pages.

Here's the catch, though: to scrape data, you'll need a reliable tool, such as ScrapingBee. Since Google Images implements strong anti-scraping measures, you won't be able to get images without a strong infrastructure.

Our API has everything to fight the protections: JavaScript rendering, CAPTCHA solving, a pool of residential proxies, and other built-in features.

All you need to do is sign up, and I'll walk you through the rest of the steps of creating a Google Images scraper. Let’s dive in.

Quick Answer (TL;DR)

The fastest way to scrape Google Images search is with a single API call to ScrapingBee. Since our service handles JavaScript rendering and proxy rotation automatically to avoid blocks, you won't need any other web scraping tools.

Here’s a quick Python example:

import requests
import csv
import pandas as pd

API_KEY = "YOUR_API_KEY"   # ← replace
query = "sunset over mountains"

resp = requests.get(
    "https://app.scrapingbee.com/api/v1/store/google",
    params={"api_key": API_KEY, "search": query, "search_type": "images"},
    timeout=30,
)
try:
    resp.raise_for_status()
except requests.HTTPError as e:
    print("Error:", e, resp.text)
    raise

data = resp.json() or {}
images = data.get("images", [])

image_rows = []
for image in images:
    image_rows.append({
        "title": image.get("title") or "",
        "full_size_url": image.get("url") or "",
        "thumbnail_url": image.get("thumbnail") or "",
        "source_page": image.get("source") or "",
        "alt_text": image.get("alt") or "",
        "image_dimensions": f"{image.get('width','')} x {image.get('height','')}".strip(),
    })

# Sample output
for img in image_rows[:5]:
    print(f"Title: {img['title']}")
    print(f"Full-size URL: {img['full_size_url']}")
    print(f"Source: {img['source_page']}")
    print(f"Alt text: {img['alt_text']}")
    print(f"Dimensions: {img['image_dimensions']}")
    print("-" * 40)

# Save only if there is data
if image_rows:
    with open("scraped_images.csv", "w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=list(image_rows[0].keys()))
        writer.writeheader()
        writer.writerows(image_rows)

    df = pd.DataFrame(image_rows)
    print(df.head())
    df.to_csv("scraped_images_dataframe.csv", index=False)
else:
    print("No images found.")

Continue reading if you need to run a more advanced Google Images data. I'll explain how to set up the environment, extract data, and even download images.

How to Scrape Google Images with ScrapingBee

Building a reliable Google Image scraper requires handling dynamic content and avoiding detection. Additionally, an effective image scraper must be able to extract image URLs, titles, source information, and related image data.

Our Google Images API takes care of these challenges, letting you focus on using the data rather than fighting with technical hurdles.

When you need to scrape data, proper parsing is essential. Let’s break down the complete process into manageable steps:

1. Set up your account and get your API key

2. Make your first request to Google Images

3. Parse the response to extract data based on search criteria

4. Handle pagination for more image content

5. Export the data to JSON format and use the collected results.

Let's start with the first step.

Set Up Your Environment

First, you’ll need to create an account on the platform’s website. The free trial provides enough credits to test the API and see how it works with Google Images.

• Go to ScrapingBee.com and sign up.

• Once logged in, you'll find your API key in the dashboard.

Now, make sure to download and install Python. During installation, check the box: "Add Python to PATH".

Verify your installation:

# Verify Python
python3 --version  # or: python --version (Windows)

Then, set up your enviroment. This keeps your project isolated:

# Create and activate a virtual environment
python -m venv scraping-env

# Mac/Linux:
source scraping-env/bin/activate

# Windows (PowerShell):
.\scraping-env\Scripts\Activate.ps1

# Windows (cmd):
scraping-env\Scripts\activate.bat

Install Required Libraries

Now you can install theScrapingBee Python SDK to simplify your API calls, as well as other needed libraries:

pip install scrapingbee requests beautifulsoup4 pandas

Great, it's time to dive right into Google Image search results.

Make an API Request

Let’s make our first request to scrape Google Images. Our API endpoint makes this process straightforward:

import requests

API_KEY = "YOUR_API_KEY"  # ← replace
query = "sunset over mountains"

params = {
    "api_key": API_KEY,
    "search": query,
    "search_type": "images",
    # Optional tuning:
    # "country_code": "us",
    # "device": "desktop",
    # "nb_results": 20,
}

resp = requests.get("https://app.scrapingbee.com/api/v1/store/google", params=params, timeout=30)
resp.raise_for_status()
data = resp.json()
images = data.get("images", [])
print(f"Fetched {len(images)} images")

Don't forget to replace YOUR_API_KEY with your actual ScrapingBee API key.

This code takes a query parameter value for your search term and an optional parameter to restrict search results.

The API handles all the complex parts of the request:

• Rendering JavaScript to load image results by default.

• Setting appropriate headers to avoid detection

• Managing cookies and session data

• Structuring the response in a usable format

You can learn more about how to scrape Google Search results using similar techniques.

Extracting Image Data

Once you extract image data from the response, you can process it for various applications. The API returns a JSON format with all the image information you need:

# Continuing from 'data' in the previous JSON API snippet:
image_rows = []
for img in data.get("images", []):
    image_rows.append({
        "title": img.get("title") or "",
        "full_size_url": img.get("url") or "",
        "thumbnail_url": img.get("thumbnail") or "",
        "source_page": img.get("source") or "",
        "alt_text": img.get("alt") or "",
        "image_dimensions": f"{img.get('width','')} x {img.get('height','')}".strip(),
    })

With this code snippet, our Google Image Search API extracts all the image metadata, such as:

• Image title (often contains image description)

• Full-size image URLs

• Source website URL

• Alt text when available

• Image URLs

• Image dimensions

You can adjust this code to extract only the links or alt text from Google Images data.

The JSON response structure makes it easy to access exactly what you need from each image result. You can even save Google images to your device, but more on that in this tutorial: Download an Image with Python.

Handling Pagination

Pagination Handling is essential for your Google Images scraping project if you want comprehensive data collection.

With ScrapingBee’s JSON endpoint, you paginate using the page parameter (0-based) and the nb_results page size:

import requests

API_KEY = "YOUR_API_KEY"
query = "sunset over mountains"

all_images = []
for page in range(0, 5):  # first 5 "pages"
    resp = requests.get(
        "https://app.scrapingbee.com/api/v1/store/google",
        params={
            "api_key": API_KEY,
            "search": query,
            "search_type": "images",
            "page": page,        # use page if supported
            "nb_results": 20,    # page size if supported
        },
        timeout=30,
    )
    resp.raise_for_status()
    data = resp.json() or {}
    all_images.extend(data.get("images", []))

print(f"Total images collected: {len(all_images)}")

This code handles both parameters needed for pagination:

• page: 0-based page index for results returned by the API.

• nb_results: number of results per page.

Using these parameters together lets you navigate through all available Google Search results.

Exporting and Using Image Data

After collecting image data, you’ll want to save it for later use. Let’s create a function to export the results to a CSV file:

import csv, pandas as pd

# Write detailed rows
if image_rows:
    with open("images.csv", "w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=list(image_rows[0].keys()))
        writer.writeheader()
        writer.writerows(image_rows)

    df = pd.DataFrame(image_rows)
    print(df.head())
    df.to_csv("google_images_dataframe.csv", index=False)
else:
    print("No image rows to save.")

In addition to a CSV file, you can also convert the data to a pandas DataFrame for analysis:

import pandas as pd

df = pd.DataFrame(all_image_urls, columns=["image_url"])
print(df.head())
df.to_csv("google_images_dataframe.csv", index=False)

This gives you the flexibility to work with the data in various formats or load it into Google Sheets for sharing.

Optimizing Requests to Avoid Blocks

When sending HTTP requests to Google Images, you need strategies to avoid getting blocked. ScrapingBee's API handles this automatically with several features, so you don’t have to worry about how to avoid getting blocked when scraping Google Images data:

1. Premium proxies: The service rotates IPs from a pool of residential and datacenter proxies

2. Browser fingerprinting: Requests appear to come from real browsers

3. Request throttling: Automatic rate limiting to avoid triggering anti-bot systems

You can further optimize your requests with these parsing parameters:

params = {
    "api_key": API_KEY,
    "search": query,
    "search_type": "images",
    "country_code": "us",  # optional targeting
    "device": "desktop",   # or "mobile"
    "nb_results": 20
}

These settings help you get more relevant results while maintaining a low profile. With our web scraping API, you can avoid the complex parts and get the data you need.

Scrape Google Images at Scale and Without the Hassle

Scraping Google Images doesn’t have to be a challenge. With ScrapingBee, you can send HTTP requests using a simple API call and get a complete API response.

Through this process, you extract image URLs, titles, source links, and alt text – all in JSON or DataFrame formats. There's no need for a screen print or setting up a headless browser.

Whether you're running a query image search, applying search operators like filetype or site, or parsing both the parameters from a given search query, ScrapingBee does the heavy lifting. From rendering JavaScript to handling pagination, our image scraper handles it seamlessly.

Use the output data to download images and filter based on image description, size, or context parameter. All the code you need is in this tutorial and our documentation.

Frequently Asked Questions (FAQs)

Can ScrapingBee scrape all Google Images results?

Yes, our Google Image search API can scrape hundreds of Google Images results by using pagination. It handles the technical challenges of rendering JavaScript and avoiding blocks, allowing you to run multiple search queries and apply search filters for successful web scraping.

How do I avoid being blocked when scraping Google Images?

ScrapingBee is an efficient Google Images scraper that avoids blocks with premium proxies, browser fingerprinting, and request throttling. You don’t need to manage proxies or rotate user agents manually - our web scraping API takes care of these details to ensure reliable web scraping.

Is JavaScript rendering required for Google Images scraping?

Yes, Google Images heavily relies on JavaScript to load content. The API automatically handles JavaScript rendering, executing all the necessary scripts to ensure you get images from Google.

How can I save the scraped images?

After getting image URLs from the web scraping API, you can download images with Python using the requests library. Simply iterate through your results and save each image to your local storage or cloud storage solution.