How to extract entities from Google Images on autopilot with AI

Table of Contents

As an SEO professional, aren’t you tired of manually locating, searching and selecting Alt tags and titles for your website or blog images

Would you like to improve your SEO strategy by optimizing your visual content

I’m sure you would. 

If you have ever used Google images to find the best Alt tags for your photos, you already know that this is one of the best sources from which to extract entities to use on your website.

That’s why we have developed a very simple script that allows you to extract, sort and generate an automatic table with all the relevant entities for a search.

This will make it easier for you to tag and organize your visual content to improve your search engine rankings.

In this post, we will explore the advantages of using our entity extractor for Google Images, how it works and how it can help SEO professionals like you to optimize your images for better SEO strategy. 


Get ready to transform the way you work your SEO for images. 

And improve the search engine rankings of your multimedia content.

What is an entity extraction tool for Google Images?

Our Google Images entity extraction tool is a Python script in Google Colab that automatically parses and extracts the entities present in a Google Images search. 

These entities can include objects, people, places and other visual elements. And they are very useful for use in tags, descriptions and titles associated with multimedia elements.

When used wisely, this makes it easier for search engines to understand your content.

Our script uses advanced AI algorithms to recognize and categorize the entities collected in Google images, providing a wealth of information to SEO experts and content creators. 

This makes it possible to optimize visual content for search engines faster.

The importance of entity collection in SEO images

Overall, an entity extraction tool for images is a valuable asset for any SEO expert who wants to improve their search engine rankings and optimize their visual content. 

Thanks to its features and time-saving capabilities, this tool is a must-have for any serious content creator who wants to stay ahead of the competition.

Keep in mind that for certain niches, optimized images and visual carousels are a must to rank as high as possible.

Moreover, this is a SEO field known for being often neglected, due to lack of time and resources.

So, in many cases a virgin field that we can exploit in our favor.

What do you need to extract entities from a query?

Actually, all you will need are 3 things:

  1. A seed keyword such as: oil fryers.
  2. Your OpenAI API KEY
  3. Your SERP API

And of course, install the Google Colab dependencies in order to run the script properly.

If you just got lost with these instructions, don’t worry.

We’ve got you covered.

Our colleague Luis Fernandez has prepared a video in which he explains step by step how our Colab works and the ins and outs behind it.

You can watch it right below:

As you can see, it is very simple. 

Take into account that this video is part of a series dedicated to artificial intelligence, programming and digital marketing that we have made in iSocialweb to boost productivity and automate SEO.

Now let’s see how it all works.

How does the entity classifier extractor for Google images work?

Our entity extractor for Google Images is a simple script of less than 50 lines of code that allows you to work with entities based on the images indexed by Google.

The script uses an API called “SERP API” together with the OpenAI API.

Both, used together, allow extracting and classify entities. 

SERP API is in charge of extracting the information from Google, while OpenAI, using GPT artificial intelligence, will be in charge of classifying and grouping the extracted entities.

In addition, the script installs the necessary dependencies to access Google search results:

  • !pip install google-search-results
  • !pip install google-api-python-client

An alternative to SERP API is to use Web Scraping, but it requires managing Proxies, paying attention to changes and self-programming.

We mention this because it can be somewhat cheaper to use Web Scraping techniques, if you know what you are doing and if we are talking about thousands of words.

In any case, once the APIs have been entered into the Colab, we only need to enter the keyword for which we want to obtain the related entities that Google already provides.

Right after that, our Colab will take these entities and sort them by GPT, in bulk.

The output format will show the entity name, followed by the group name, with multiple entities belonging to the same group listed in the same row.

Something similar to this:

image2 2

The table is very useful for quick filtering, but human review is necessary.

If you don’t like the format of the table you get, you can always use ChatGPT to generate a table in Markdown.

To do so, just copy and paste the prompt used by the Colab. Here you can see the text of the prompt we used:

 				 					'Given the following entities about ' + keyword + ' : ' + str(entities_images) + ' Classify them by grouping them with their corresponding name depending on the type of entity. If there is no clear classification, use "No classification". Return the result in table format with two columns: entity name | group.' 				 			

Add the extracted entities from the Colab to the prompt until it looks something like this:

 				 					Given the following entities about oil fryers: ['industrial', 'water', 'gas', 'double', 'fries', 'electric', '1 litre', 'air', 'kitchen', 'lively', 'large', 'sankey', 'professional', 'recco', 'morley', 'premium', 'restaurant', 'semi', 'media markt', 'air fryer', 'oil-less fryer', 'industrial fryer', 'movilfrit fryer', 'cecotec fryer', 'electric fryers', 'air fryer', 'best fryers'].  Classify them by grouping them with their corresponding name depending on the type of entity. If there is no clear classification, use "No classification". Return the result in table format with two columns: entity name | group.   				 			

ChatGPT will return something similar to this:

image4 1

This is an unnecessary post, but we recommend it if you are managing several hundred words and need to have the entities sorted in a much more orderly and visual format.

This way, you will be able to analyze at a glance the summary table with all the entities classified by groups.

And take them to Excel if necessary for analysis.

Benefits of using an entity extractor for images

One of the main advantages of using an entity extractor for Google images is its ability to automatically collect and classify the relevant terms or words that search engines associate with certain content.

In other words, they provide us with a summarized list of the entities related to a given search and which must appear in the content in some way or another.

Therefore, using these entities increases the chances of appearing in the first positions of the search results.

Advantages of our Google Colab

Compared to other image analysis tools, our script for Google Images excels in two aspects:

1. One of the main advantages is its accuracy

The tool uses GPT to recognize and categorize the extracted entities in an automated way, resulting in more accurate results than other applications.

2. Another advantage is its ease of use

Google Colab is designed to be easy to use, with a simple interface that makes it easy to upload and analyze images based on a single input: the keyword. This can save SEO experts time and frustration, allowing them to quickly and easily optimize their visual content.

Frequently asked questions

The Google Image Entity Extraction Script is highly accurate in recognizing objects, places and people from Google images.

No, Google’s entity extraction tool for images is only used for image search and analysis.

No, the entity extraction tool for Google images only supports the Google image search engine.

Bottom Line

Overall, the entity extraction tool for Google Images is a valuable resource for SEO experts who want to improve their search engine rankings and optimize their visual content. 

Thanks to its numerous advantages and use cases, this tool is a must-have for any serious content creator who wants to stay ahead of the competition.

With its high precision, the script can help you improve your website’s SEO for images and boost search results for related content. 

Add to this, the fact that it’s a free app, and you’ll see that it’s all advantages, 

In short, we encourage you to try it if you have not already done so and of course to compare the results with other similar applications.

You are sure to improve your results in Google Images.

Here are some additional resources for entity extraction:

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Alvaro Pena
Head of SEO and co-CEO en iSocialWeb

Co-CEO and Head of SEO at iSocialWeb, an agency specialized in SEO, SEM, and CRO that manages over +350M organic visits per year with a 100% decentralized infrastructure. Also, the founder of Virality Media, a company with over 150 million monthly active visits across various sectors and industries. A Systems Engineer by training and an SEO by vocation. An avid learner, AI enthusiast, and dreamer of prompts.

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