As an SEO professional, are 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? If you have ever used Google Images to find the best alt tags for your photos, you already know it is one of the best sources from which to extract entities for your website.
That is why we developed a simple but powerful script that allows you to extract, sort and generate an automatic table with all the relevant entities for a search. This makes it easier to tag and organize your visual content and improve your search engine rankings.
In this post, we will explore what our entity extractor for Google Images with AI does, how it works, how to run it step by step, and how it can help SEO professionals like you get more from your visual content.
What is an entity extraction tool for Google Images?
Our Google Images entity extraction tool is a Python script that runs inside Google Colab. It automatically parses and extracts the entities present in a Google Images search. These entities can include objects, people, places, brands and other visual elements. When used wisely, they make it easier for search engines to understand your content.
The script uses generative AI and large language models (LLMs) to recognize and categorize the entities collected from Google Images. Specifically, it draws on Google's AI infrastructure, including APIs compatible with models like Gemini and Vertex AI, to analyze visual content at a level of accuracy that goes far beyond simple keyword matching. This means the tool does not just read metadata. It understands context.
For SEO professionals and content creators, this translates into faster, smarter optimization of visual content.
Why entity extraction matters for image SEO
Search engines like Google do not just read text. They analyze the entities associated with every piece of content on your site, including images. When you correctly label your images with relevant entities (objects, locations, people, concepts), you help Google connect your content to the right search queries.
Manual tagging is slow, inconsistent and hard to scale. An AI-powered entity extractor solves all three problems at once. It gives you a consistent, data-driven set of labels based on what Google itself recognizes in image search results, which is exactly the signal you want to align with.
The AI technology powering the tool
The script uses large language models (LLMs) and Google's generative AI capabilities to process and interpret visual search data. Rather than relying on simple pattern matching, the underlying model understands semantic relationships between entities, which produces more relevant and contextually accurate results.
The tool also incorporates OCR (Optical Character Recognition) capabilities, which means it can detect and extract text-based entities directly from image content and metadata. This is particularly useful when working with product images that contain text overlays, infographics, screenshots, or any image where visible text carries SEO value. The OCR layer reads that text, extracts the entities within it, and adds them to your output alongside visually detected entities.
How to run the entity extractor: Step-by-step
Running the script is straightforward. You do not need to be a developer. Here is how to get started:
- Open the script in Google Colab. Click the link provided to open the ready-made notebook in your browser. No installation required.
- Enter your search query. In the designated input cell, type the keyword or topic you want to extract entities for. For example, "running shoes" or "mountain landscape photography".
- Set your parameters. You can adjust the number of images to analyze and specify any filters such as image type or region. Default settings work well for most cases.
- Run all cells. Click "Runtime" then "Run all". The script will connect to the Google Images search, parse the visual results, and pass the data through the AI model for entity recognition.
- Review the output table. Within seconds, the script generates a structured table of entities, sorted by relevance and frequency. You can export this directly to CSV or JSON.
The entire process typically takes under two minutes for a standard query. For larger batch runs, processing time scales with the number of images analyzed.
Output format: JSON and structured data
One of the most practical aspects of this tool is its structured data output. Results are returned in JSON format, making them immediately usable in developer workflows, SEO platforms and content management systems.
A sample output looks like this:
- entity: "trail running shoe"
- category: "product"
- frequency: 34
- relevance_score: 0.92
- source_images_count: 18
Each entity is tagged with its category (object, person, place, brand, concept), its frequency across the analyzed images, and a relevance score derived from the AI model. You can also export the full results as a CSV table if you prefer to work in spreadsheets. This structured output means you can paste entities directly into your CMS, feed them into a Google Sheet, or pipe them into a larger SEO automation workflow.
Document classification and content categorization
Beyond simple entity lists, the script also supports document classification and splitting. This means it can group extracted entities into logical content categories, helping you understand not just what entities exist, but how they cluster thematically. For a travel blog, for instance, it might split results into categories like "geography", "activities" and "accommodation", giving you a ready-made content architecture for your image library.
This classification layer makes the tool especially useful for editorial teams managing large archives of photos organized by topic or campaign.
Integration with other platforms and APIs
The JSON output format makes integration straightforward. Here are some of the most common ways SEO professionals connect the tool to their existing stack:
- Google Sheets: export the JSON or CSV output directly into a sheet for team collaboration and manual review.
- Google Search Console: cross-reference extracted entities with your existing query data to identify gaps in your image SEO coverage.
- BigQuery: feed entity data into BigQuery for large-scale analysis across multiple sites or image libraries.
- Vertex AI: connect to Vertex AI pipelines for more advanced processing, custom model evaluation or automated tagging workflows.
- Third-party SEO platforms: tools that accept CSV or API input (such as content audit or tagging platforms) can ingest the structured output directly.
If you work in a larger team or manage an enterprise-level image library, these integrations are what turn a useful script into a production-ready tool.
Custom model fine-tuning for your niche
The default AI model works well across most topics, but if you operate in a specialized niche (medical photography, legal document images, industrial product catalogs), you may want more precise entity recognition for your specific domain.
The script supports custom fine-tuning. You can feed it your own labeled image data or domain-specific documents to train the underlying model on terminology and visual patterns relevant to your field. This improves accuracy significantly for niche use cases where generic models may miss industry-specific entities or misclassify them.
Fine-tuning does not require deep machine learning expertise. The Colab notebook includes a configuration section where you can point the model toward your custom dataset and run a lightweight training pass before executing the main extraction.
Scalability: Processing images at scale
For individual bloggers or small sites, running the script on one keyword at a time is perfectly sufficient. But for SEO professionals managing large image libraries, the script is built to handle batch processing.
You can supply a list of dozens or hundreds of search queries in a single run. The script processes each one in sequence, aggregates the entity data, and produces a combined output file. This is particularly valuable for:
- E-commerce sites with hundreds of product categories.
- News archives with thousands of editorial photos.
- Travel or lifestyle sites with deep image libraries organized by destination or theme.
The cloud-based nature of Google Colab means you are not limited by your local machine's processing power. For very large runs, you can upgrade to a Colab instance with more compute resources without changing the script itself.
Real-world use cases
Product image gallery for an e-commerce store
An online retailer selling outdoor gear can run the extractor across search terms like "hiking backpack", "waterproof jacket" and "trekking poles". The output gives them a full entity map for each product category, which they then use to populate alt tags, image file names and surrounding text. The result is better product image visibility in Google Images and Shopping.
Editorial photo archive for a news or media site
A media publisher with thousands of archive images can batch-process queries related to their main topics (politics, sports, culture). The extracted entities help them retroactively tag older images that were uploaded without proper metadata, recovering lost SEO value across the archive.
Travel blog image optimization
A travel blogger covering Southeast Asia can run queries for each destination they cover. The entity extractor pulls location names, landmarks, activities and cultural references that appear consistently in Google Images results for those searches. The blogger uses these entities to write more accurate and search-friendly captions, titles and surrounding content for every post.
Pricing and free access
The script is free to access and run via Google Colab. You do not need a paid subscription to get started. All you need is a Google account. The underlying API calls may consume a small amount of Google Cloud credit depending on the volume of your queries, but for typical SEO use the costs are minimal.
We also offer a free trial with starter credits so you can test the full feature set, including batch processing and JSON export, before committing to any paid tier. Details are available on the tool's access page. There are no hidden costs for basic usage, and the script itself is openly available for you to inspect and adapt.
Why this tool stands out for SEO professionals
There are plenty of entity extraction tools on the market, but most of them focus on text documents. This script is built specifically for Google Images search data, which means the entities it surfaces are exactly the ones Google associates with visual search queries in your niche. That is a direct line to the signals that matter for image SEO.
Combined with structured JSON output, OCR text detection, LLM-powered classification, batch processing and platform integrations, it gives SEO professionals a complete toolkit for visual content optimization, without needing to manually comb through hundreds of search results.
Whether you manage a small blog or a large-scale content operation, this tool adapts to your workflow. Start with a single query, or plug it into an automated pipeline. Either way, your image SEO will be built on real data rather than guesswork.