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6 Best Image Recognition APIs

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Working with a large volume of images is even possible without some sort of image recognition API in place. 

However, without a specific image processing API, operations like finding related images or identifying landmarks are practically impossible.

In this article, we’ll focus on image-processing APIs. There are many OCR APIs out there.

Some image recognition APIs still work with other computer vision applications. Because of this, if you’re creating a new computer vision tool, it’s still worthwhile to have a look at them.

What Is Image Recognition API?

The human brain achieves image recognition by examining each pixel in an image. Image recognition API also extracts relevant information in the same way that humans do. AI cams trained in computer vision can detect and recognize many objects.

Image recognition API, in the context of machine learning, can be defined as the ability of a set of software tools to identify objects, places, people, writing, and actions in images. 

In practice, computers with machine vision technologies, in combination with a camera and AI, can achieve image recognition.

How Does Image Recognition API Work?

It’s not easy for machines and software applications to learn from the example that comes naturally to human beings. Image recognition ultimately involves developing methods that attempt to reproduce the capability of human vision.

To recognize any object, machines must learn about its distinguishing features from many of its images from various angles. It’s a complex process and takes a lot of time and effort.

Where Are Image Recognition APIs Used?

They can be used to:

  • Labeling the content of images with meta-tags
  • Self-driving cars and accident-avoidance systems
  • Searching for image content and controlling autonomous robots
  • Protecting forests by surveilling with UAVs
  • Military Surveillance to protect the border and critical infrastructure

These are just a few of the nearly infinite applications of image recognition APIs.

How To Choose An Image Recognition API?

  • Visual Analysis Features: By exploring product pages and documentation, we can know which entities the API can recognize and detect.
  • Billing: based on the projected workload, you can determine.
  • API Usage: APIs only become helpful when developers know how to use them. Relevant tutorials are a must.
  • Support: Must be available 24/7 via multiple channels for technical support.

What Are The 6 Best Image Recognition APIs?

Here are some of the best image recognition APIs, covering a wide range of applications and features.

1. Filestack Image Processing API

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Filestack Image Processing API is a suitable toolkit for a large amount of photo processing. Its image processing API can store, compress, and convert files instantly and automatically.

Additionally, its copyright detection helps preserve any online content’s rights.

Filestack’s Processing API and Image Intelligence Suite can immediately find copyright-protective images when users upload one or a million.

Regarding inappropriate content, Filestack also uses image processing APIs. It can immediately recognize inappropriate content and characters.

It can also automatically integrate with file-sharing platforms like Google Drive, Dropbox, and Facebook.

A few more common distinctive features like tag videos, crop size or resize images, compress, or rotate images make Filestack competitive.

2. CloudVision API


Google’s CloudVision API  is proximate to a plug-and-play image identification API. It’s used to detect the predominant color from an image and is pre-configured to handle the image identification motives.

It allows developers to easily integrate image detection features within applications. It includes image labeling, face, landmark detection, optical character recognition, and explicit content tagging.

Optical Character Recognition (OCR) is the most crucial feature of the Google CloudVision API for any file, like JPEG and PDF. It can identify handwritten and printed text.

Google provides extensive data and machine-learning libraries. That’s why using libraries can detect landmarks and identify objects in images.

Google’s CloudVision API is a little bit expensive. So, if you’re ready to pay the fees, you can go for it.

3. Microsoft Image Processing API

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Microsoft Computer Vision API for image processing is a cloud-based API that allows developers access to algorithms for processing images and returning information.

It uses machine learning algorithms to classify images. It’s not only specialized for doing complex tasks, but it also works for a general-purpose API.

Different companies like Google, Amazon, IBM, and others offer this machine learning service in the cloud. It saves the user from having to make their database of image processing and neural networks and buying the infrastructure to conduct all from that.

The Microsoft API uses its massive infrastructure and machine learning models trained with many images. Neural networks (deep learning) classify the images when the developer posts an image there.

Its price depends on the territory and the number of transactions.

4. Amazon Rekognition

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Amazon Rekognition comes with a simple and easy-to-use API. It is used to examine any picture or video file kept in Amazon S3. It’s user-friendly because it requires no machine learning expertise to use. It is also highly scalable and built with deep learning technology. Also, it is used to analyze billions of images and videos uploaded daily. It is constantly learning from new data. So, Amazon’s scientists must continually add new labels and facial comparison features to the service.

When Amazon Rekognition API gets an image or video as an input, it can identify objects, people, text, scenes, and activities within seconds.

Inappropriate Content? You don’t need to worry, because Amazon Rekognition can detect it automatically.

It is suitable for various situations, including user identification, cataloging, people counting, and public safety, thanks to its highly accurate facial analysis, face search, and comparison features.

With several payment levels, it also offers a free tier, which makes it noteworthy. You can get a quote via the pricing page if you’re interested in more than just their free service.

5. Clarifai

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Clarifai is one of the leading deep learning AI life cycle platforms for image processing.

It includes a number of pre-built computer vision models for the analysis of visual input.

It’s also user-friendly. Simply submit your media, and Clarifai will provide predictions based on the model you are currently using.

There are also profitable elements in Clarifai. For instance, it has one of the most thorough systems for identifying fashion. Thousands of fashion accessories and items can be recognized using the Fashion computer model.

Like other image recognition APIs, it can detect explicit content. Moreover, it can identify celebrities, recognize faces and determine the dominant color of an image.

6. IBM Watson Visual Recognition API

The IBM Watson Visual Recognition for Cloud is an image recognition API that allows programmers to make intelligent applications that perform visual content analysis.

Using machine learning algorithms, neural networks, and image identification, developers can build, train, and test models. It’s all about the general model, which provides a classification for thousands of predefined objects.

To get started with a trial, there is a free plan of the Watson Assistant service, which is capped at 10,000 free API calls.


Image recognition APIs extract relevant information in the same way that humans do. Today image recognition APIs are used in many use cases. Therefore, finding the best image recognition APIs in our development projects is essential. In this article, we discussed the six best APIs. Considering all the features, it is clear that Google’s CloudVision API is the best in its class.



Kossi Adzo is the editor and author of He is software engineer. Innovation, Businesses and companies are his passion. He filled several patents in IT & Communication technologies. He manages the technical operations at

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