Google Vision: Image Recognition Analysis Tool

5 min readJul 13, 2022

Using artificial intelligence, Google’s image classification tool sorts and labels images.

Google Vision can automatically classify images but can also be used as a stand-alone tool to see how an image detection algorithm views your images and what they’re relevant for.

It’s fun to see what Google’s image-related algorithms can do even if you don’t use the Google Vision API to detect and classify images at scale; you can upload images and see how Google’s Vision algorithm classifies them.

Feel free to explore Google Vision: upload an image and see how it is classified.

What Algorithm does the Cloud Vision Tool reflect?

This is merely a model for machine learning, not a ranking algorithm in the traditional sense.

That’s why this tool won’t tell you anything about Google’s image ranking algorithm, but it is an excellent tool for learning about how Google’s AI and Machine Learning algorithms can interpret images, and it will give you a sense of how advanced today’s vision-related algorithms are.

As a result of this tool, it is possible to learn more about how a computer might interpret an image, and how well it matches the overall thematic of an image.

What are the Benefits of using an Image Classification Tool?

Images can have a significant impact on a webpage’s search visibility and click-through rate (CTR). They help direct site visitors who are doing research on a particular subject to the most relevant pages.

As a result, in some cases, using images that are relevant to search queries can help quickly communicate that a webpage is relevant to what a person is searching for. Understanding how an algorithm interprets what is in a picture can be done with the help of the Google Vision tool.

Google’s image analysis tools categorize images in five different ways:

Google Vision: Objects

The “objects” algorithm shows what objects are present in the image, such as glasses, horses, persons etc. and the accuracy percentage.

Google Vision: Faces

Using the “faces” tab, you can see how the image is expressing its emotions. There is a reasonable degree of accuracy in this result. With a confidence level of 96%, the AI describes the expression on the face as one of surprise.

Google Vision: Labels

You can see details like ears and mouth, but you can also see more conceptual aspects like portrait or photography by clicking on the “labels” tab.

In this case, Google’s image AI shows how well it understands the content of an image.

Google Vision: Properties

The purpose of this tool isn’t immediately apparent, and it may appear to be utterly pointless. When it comes to featured images, the colors of an image can have a significant impact on its success.

You should look out for images that have a wide range of colors, as this indicates that the image has been poorly chosen and is too large. Furthermore, images with a darker color range are likely to have larger file sizes.

Google Vision: Safe Search

The image’s ranking in Safe Search is based on its risk of containing harmful content. The following are descriptions of images that may be dangerous: Adult, Spoof, Medical, Violence, Racy.

There are filters in Google search that look for potentially harmful or offensive content. This is why the Safe Search section of the tool is critical, as if an image unintentionally triggers a safe search filter, the webpage may not rank for potential site visitors who are searching for the content on that page.

In the screenshot shown below, a photo of racehorses on a racetrack was analyzed. There is no medical or adult content in the image, according to the tool.

Google Vision OCR

OCR, the process of converting handwritten or printed text into machine-encoded text, has long been a focus of computer vision research because of the wide range of applications it can serve.

Google Cloud Vision OCR is a part of the Google Cloud Vision API that helps extract text from images. There are two annotations in particular that can be used to aid in character recognition:

Text Annotation: It extracts and produces machine-encoded text from any image (e.g., photos of street views or sceneries). To a lesser extent, the model’s ability to read words in a variety of styles is strengthened by the fact that it was built from the ground up to work in a variety of lighting conditions. The returned JSON file contains the entire string as well as the individual words and their respective bounding boxes.
Document Text Annotation: This is best suited for documents with a lot of text (e.g., scanned books). While this allows for the reading of smaller and more focused texts, it is less flexible when dealing with nature related images. The output JSON file includes information like paragraphs, blocks, and breaks.