Google

February 5, 2026

door Autom Team

Scrape Google Images using Python

Visual search has come a long way, and with it, the demand for extracting image data from the web has grown sharply. From training datasets to market research and content analysis, scraping images based on clear visual intent is now a common requirement.

Among all available sources, Google Images remains one of the richest collections of visual data on the internet. However, scraping images for a specific keyword at scale is not straightforward. Google actively restricts automated access, making large-scale extraction unreliable and prone to frequent blocks when done using traditional scraping methods.

In this article, we walk through how to unlock this massive visual archive by scraping Google Images using Python with our Google Images Search API. You’ll learn how to fetch image results programmatically without running into rate limits or blocking issues.

What data can be scraped from Google Images?

Google Images provides more than just image files. When scraped correctly, it can return a structured set of visual and contextual data that is useful across multiple use cases. Below are the most common data points you can extract.

Image URLs
This is the most frequently scraped data from Google Images. These URLs point to the actual image files hosted on external websites and can be used to preview or download images programmatically.

Image titles
Image titles and related metadata reveal how images are labelled and described online. This can help in understanding how visual content is associated with specific keywords or topics.

Image thumbnails
Google Images also returns thumbnail versions of images. These are smaller previews typically shown in search results and are useful when you need lightweight visual references instead of full-size images.

Source page URLs
Along with image URLs, you can extract the URLs of the webpages where those images appear. This adds important context and allows you to trace each image back to its original source.

Image captions and descriptions
Many image results include captions or descriptive text. Scraping this information can provide insight into the subject, usage, or setting of an image.

These are some of the most common types of data that can be collected from Google Images. The exact fields you choose to extract will depend on your use case and the insights you’re trying to derive.

How to scrape Google Images using Autom’s API

Autom’s Google Images API allows you to fetch Google Image results programmatically using a simple POST request. You only need your API key, a search query, and optional parameters such as country, language, and page number. The API returns structured image data in JSON format, which can then be processed or stored using Python.

Step 1: Set up your Python environment

Make sure Python 3.7+ is installed on your system. You’ll need the requests library to send HTTP requests and pandas to store the extracted data.

Install the required dependencies:

pip install requests pandas

Step 2: Import required libraries

Create a new Python file and import the necessary libraries:

import requests
import pandas as pd

Step 3: Define the API endpoint and headers

Autom uses API key–based authentication. Your API key must be passed in the request headers.


API_KEY = "YOUR_AUTOM_API_KEY"

url = "https://autom.dev/api/v1/google/images"

headers = {
    "Content-Type": "application/json",
    "x-api-key": API_KEY
}

Step 4: Create the request payload

The payload defines what you want to search for and how the results should be localized or paginated.


payload = {
    "query": "cute cats",
    "gl": "us",
    "hl": "en",
    "page": 1
}

query specifies the image search keyword

gl sets the country location

hl sets the language

page controls pagination

Step 5: Send the request

Send a POST request to Autom’s Google Images endpoint:

response = requests.post(url, headers=headers, json=payload)

response.raise_for_status()

data = response.json()

If the request is successful, the response will contain a list of image results under the images key.

Step 6: Extract image data from the response

You can now extract useful fields such as image URLs, source pages, titles, and dimensions.


images = []

for image in data.get("images", []):
    images.append({
        "image_url": image.get("url"),
        "source_page": image.get("link"),
        "title": image.get("title"),
        "domain": image.get("domain"),
        "position": image.get("position"),
        "width": image.get("image_width"),
        "height": image.get("image_height")
    })

Step 7: Save the results to a CSV file

Convert the extracted data into a DataFrame and save it as a CSV file:


df = pd.DataFrame(images)

df.to_csv("google_images_results.csv", index=False)

print("Google Images data saved successfully.")

This will generate a CSV file containing structured Google Image results that can be used for analysis, training datasets, or downstream automation.

Complete Google Images scraper example

Let’s put everything together and scrape Google Images using Autom’s API with Python.

This example sends a request to Autom’s Google Images endpoint, extracts image data from the response, and saves it into a CSV file.


import requests
import pandas as pd

# Your Autom API key
API_KEY = "YOUR_AUTOM_API_KEY"

# Autom Google Images endpoint
url = "https://autom.dev/api/v1/google/images"

# Request headers
headers = {
    "Content-Type": "application/json",
    "x-api-key": API_KEY
}

# Request payload
payload = {
    "query": "cute cats",
    "gl": "us",
    "hl": "en",
    "page": 1
}

# Send request
response = requests.post(url, headers=headers, json=payload)
response.raise_for_status()

data = response.json()

# Extract image data
all_images = []

for image in data.get("images", []):
    all_images.append({
        "title": image.get("title"),
        "image_url": image.get("url"),
        "source_page": image.get("link"),
        "domain": image.get("domain"),
        "position": image.get("position"),
        "image_width": image.get("image_width"),
        "image_height": image.get("image_height")
    })

# Save to CSV
df = pd.DataFrame(all_images)
df.to_csv("google_images.csv", index=False)

print("Successfully saved Google Images data.")

What this script does

Sends a POST request to Autom’s Google Images API
Fetches image search results for the given query
Extracts image URLs, titles, source pages, and dimensions
Stores the results in a structured CSV file

Executing this script will generate a CSV file containing Google Image results for the specified keyword and page.

Notes

To fetch more results, increment the page parameter
Use gl and hl to localize results by country and language
The response is already structured, so no HTML parsing is required

This approach allows you to scrape Google Images reliably using Python without dealing with proxies, browser automation, or blocking issues.

Conclusion

Scraping Google Images without a dedicated solution is difficult to maintain at scale. Google actively limits automated access, and manual scraping setups often break due to layout changes, rate limits, or blocking mechanisms. This makes long-term image data collection unreliable without the right infrastructure in place.

Using Autom’s Google Images API simplifies this process by returning structured image data directly from Google search results. You don’t need to manage proxies, handle CAPTCHAs, or parse raw HTML. Instead, you can focus on collecting and using image data efficiently for your specific use case.

If you’re exploring other ways to automate Google data workflows, you may also find these guides useful: