Image Recognition Applications

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8 min

As machine learning and artificial intelligence technologies develop, the capabilities of solutions based on them expand and improve. One of the most promising areas in this area is computer vision, a digital, highly intelligent alternative to human vision, capable of capturing the features of objects and/or generating valuable insights based on them. Below, we will consider the types of image recognition applications, and also provide brief overviews of the five most advanced of them.

Key takeaways:

  • Depending on the purpose, there are supervised, unsupervised, and self-supervised image recognition apps;
  • Among the most popular image recognition applications are Google Lens, CamFind, PlantSnap, Adobe Scan, and Microsoft Seeing AI;
  • In a business context, image recognition can find dozens of use cases, including strengthening security measures, diagnostics (medical, production, and so on), implementation of personalized marketing campaigns, etc.

What Is Image Recognition?

Image recognition refers to a number of technologies, usually based on machine learning and artificial intelligence, that provide analysis, classification, and interpretation of visual information from images and videos, be it objects, scenes, faces, text, human speech, etc. Based on the results that generate these software solutions, the digital systems of which they are a part, are capable of extracting valuable and sometimes non-obvious patterns and details.

Given these characteristics of an average image search app, depending on its owner’s goals, it can solve a wide range of problems, including the following:

  • intellectualization of security measures, in particular, with facial recognition, object detection, as well as general real-time monitoring with analysis of scenes and behavior of people in the frame;
  • increasing the accuracy of the results of diagnostic procedures – at manufacturing, at industrial facilities, during a medical examination, etc.;
  • personalization of marketing campaigns based on analysis of behavioral patterns of buyers in offline stores;
  • automation of business processes in order to replace manual data entry with alternative highly intelligent solutions;
  • numerous scientific problems and scientific researches.

Of course, this is not a complete list, and, in practice, it can be significantly expanded by new, not-so-popular ways that have not been widely covered in the media. 

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Types Of Image Identification Systems

Now, let's look at the three types of image recognition systems that exist today.

1. Supervised Image Identification Systems

Such systems use hints given by people. Specifically, humans take part in tagging and categorizing a certain sample of images, then these manually processed images are fed into a training model, and the model starts self-training based on this sample. Thus, the model begins to “understand” how to analyze new, previously unprocessed images.

In terms of use cases for such systems, they typically form the basis of categorization solutions that require accurate object recognition.

2. Unsupervised Image Identification Systems

Unlike the previous type, these systems do not use hints previously left by a human. Instead, they use numerous analytical algorithms to independently identify patterns and features that may not seem obvious to the human brain. Thus, they are able to detect similarities and differences without thinking in stereotypes.

Often, such systems are used to cluster groups of images according to certain characteristics and parameters.

3. Self-Supervised Image Identification Systems

Finally, this type of system combines the two previous approaches, relying both on its own algorithms and on hints and tags obtained independently earlier. These systems quite often find their application in complex digital solutions for in-depth analysis of scenes in photo and video materials.

Popular Image Recognition Mobile Apps

In this paragraph, we propose to move from theory to practice and consider the five most popular image recognition applications.

1. Google Lens

Google Lens is one of the most popular applications for smart object analysis. Using this image recognition app is extremely simple: you need to point your video camera at the object you want to identify, be it text, a living creature, a landmark, or something else. After a few moments, you will receive a relevant answer from Google.

2. CamFind

This picture identification app will help you find out more about the objects captured by the camera lens of your smartphone. In particular, with its help, you can plunge into the history of cultural objects, works of art, logos, etc. Moreover, you will be able to check similar objects that the application will additionally bring to your search selection. Overall, this is an ideal tool for expanding your horizons.

3. PlantSnap

PlantSnap is a great software solution for naturalists who want to quickly find information about the plants they capture using their smartphone camera. This photo recognition app works similarly to the above two mobile apps: to identify an object and get a detailed description of it, you just need to point the camera.

4. Adobe Scan

Unlike the other three applications, this app for finding items by picture also converts camera-captured objects into editable PDF files. This is why Adobe Scan is often used by professionals, researchers, and students.

5. Microsoft Seeing AI

Microsoft Seeing AI quite often acts as a smart assistant for people with various visual impairments. In particular, with the help of this visual matches app, they can receive detailed information about what is happening around them (in the form of voice messages) through their personal mobile devices. The capabilities of this application cover not only the identification of objects but also reading text from physical sources.

Image Recognition Technology Use in Apps

Now, we invite you to check a special case of custom development of a service mobile app for a company working in the field of tire leasing for heavy transport.

In particular, the client needed to automate processes related to identifying the condition of leased tires and calculating rental costs based on this analysis. Considering the fact that the maintenance was carried out on site (that is, in parking spaces in the company’s service stations located throughout the country), after the introduction of innovations, these procedures had to be performed locally, using 10-inch tablets based on Android OS specially purchased for this purpose. The reason for introducing automation was simple – to minimize work with paper documentation and the associated errors and negligence on the part of company employees.

To achieve the goals set by the client, we needed to complete several tasks:

  • to develop a native mobile application for Android;
  • to bring to life all the necessary functionality: license plate recognition, tire check moderation logic, generation of wheel formulas, etc.;
  • to implement a backend for processing data collected using this mobile application.

In the context of image recognition, our team needed to implement functionality for the correct identification of vehicle license plates by pointing the tablet camera at the car license plate on the spot. Since the client refused to integrate with ready-made image recognition solutions, we turned to custom development, implementing the planned capabilities for different lighting levels, different tablet angles, and other individual inspection conditions.

Based on the formulated list of requirements for the project, we chose Kotlin, GraphQL, PHP, React, and MySQL as the main technology stack.

As a result, the product we created allowed the client to automate tire inspection and diagnostic procedures. Thanks to this, the client's company abandoned paper documentation, which took a lot of time and money and led to confusion and errors, as well as standardized its business data. Moreover, this mobile application reduced the risks associated with fraudulent inspection by digitizing the routine tasks of company employees and creating a single digital source of truth.

Currently, the application has already been successfully launched, and the WEZOM team is working on its updates. You can learn more about in this case.


We hope that we have helped you understand the features and benefits of picture recognition solutions, and now, you understand exactly what benefits they can bring to your business. In particular, if you are planning to introduce such a solution into your business processes, feel free to contact us, and we will make every effort to create a highly intelligent and precise digital product within your budget and timeframes.

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