Image recognition

Image Recognition Applications: 7 Essential Future Uses

Image recognition technology has revolutionized online visualization with its applications in facial-recognition, driverless cars, medical disease identification, and even in the areas of education. The future of image recognition applications is extensive and we discussed 7 of them.


A lot of technological breakthroughs were promised to us, till the year 2020. Although much has not been achieved as claimed, yet the advancement and implementation of revolutionary technologies in the realm of AI (Artificial Intelligence), Big Data, and Machine Learning has contributed immensely to refining Image Recognition technology further.

The dawn of driverless cars, impressive facial-recognition mechanics already implemented in various countries, and faster detection of objects in real-time with accuracy – all were made possible through image recognition algorithms, powered by machine learning.

Via Disruption Hub

One can easily predict the Image recognition market to be thriving in the coming years, at this point. The statistics estimate the IR market to reach around 3.8 billion USD by the year 2021. This is a massive hike from a mere 15.9 billion in 2016, nearly 100% increase, according to data by Markets and Markets research.

Image recognition technology has embedded seamlessly in the areas of e-commerce, content sharing (helping in moderating offensive visual content), security, healthcare, and automotive – Let’s look at the image recognition applications that software and app development companies and as a whole are working to pave the way towards a futuristic present, and near-future.

Improving Augmented Reality Gaming and Applications

Fairly new and exciting, but rapidly progressing – Augmented reality has given a whole new perspective to ‘daydreaming with your eye open’. The gaming arena has started using image recognition technology coupled with augmented reality to their advantage, as it helps to provide gamers with a realistic experience. Of course, developers benefit greatly as they can utilize image recognition in creating realistic gaming environments and characters.

Via TechGenyz

China’s popular video game, Honour of Kings, uses image processing applications in face recognition to determine the ages of their users. This entails the many variations of image recognition technologies are being utilized in the gaming landscape.

Various non-gaming augmented reality applications are boosted by image recognition, like CrowdOptics, and Blippar – crowd behavior monitoring and augmented reality advertising applications, respectively.

Assisting in the Educational System

What good is technology if it is not solving the various problems faced by humans. The Image recognition system is adding great value in the educational sector by enabling students with learning disabilities to register knowledge in a way that is easier for them. For example, applications that rely on computer vision, allow text-to-speech options –this greatly assists visually impaired or dyslexic students to read the content.

This isn’t the limitation of image recognition technology’s contribution to the student bodies. It is also helping in breaking free from the traditional teaching boundaries and equipping educators with high-tech learning tools.

Optimizing Medical Imagery

Considering this is the rise of photo-centric age, images, pictures, and video is of higher preference – Medicine field isn’t far behind with their medical data consisting of 90% of medical images, making this their largest data source in healthcare.

So connecting one dot with another, these medical images will be trained by the smart image recognition technology to revolutionize the art of diagnosis – making detection of severe diseases, including cancer easier.

Via Tourism News Live

Senior Manager for intelligent information systems at IBM Research, John Smith informed that detection of melanoma (a type of cancer), is one of the promising automated image processing applications.

Boosting Driverless Car Technology

Yes, the technology for a near-perfect smooth journey in a self-driving car is still in the works, as several incidents of autonomous vehicles crashing emerged. However, this definitely is the future of the automotive industry.

Computer vision is one of the key components fueling self-driving technology, including the enhancement of safety features. In addition, AI is implemented to identify objects on the roads; which includes other vehicles, sharp turns, people, pathways, and moving objects in general.


AI, ML, computer vision, coupled with Image recognition is actively being incorporated by many companies aiming to introduce driverless cars to their market.

Image recognition technology, in particular, is enabling speed prediction, the behavior of other moving objects, and location. Since image recognition backed by AI and ML, in a nutshell, is facilitating inanimate technology with a pair of eyes – Researchers are close to creating AI that would allow cars to see during the dark. Pretty impressive!

Predicting Consumerism Behavior

Of course, the area of brand advertisements, Ad targeting, and improving customer service was bound to benefit from the useful applications of image recognition. By targeting customer’s posted photos through IR – they can learn about their interests and consumerism behaviors. This, in turn, will help the brand’s targeting efforts to be streamlined and perfected. Since they will have the required data to target a relevant audiences and place their ads smartly.

This is another useful IR use case, second to medical imagery. The world of advertisement has been enhanced enough with the help of social media, and now with image recognition technology – users and brands will be facilitated more accurately.

Giving Machines a Vision

As discussed above, image recognition applications help identify objects, visualize them further, analyze them, and then enable machines to make decisions accordingly with respect to the visual inputs received.

Apart from the discussed uses of IR, it has been embedded in vital manufacturing and industrial processes, which opens possibilities to an impeccable production process world-wide. What more is that this technology to take visual inputs intelligently allows machines to detect defects in the manufacturing structure, without depending on human assistance. Of course, AI follows closely, operating to fetch necessary information by learning the behavior on its own.

The entire ecosystem IR ensures mitigation of risks, reliable, and error-less production processes.

Iris Recognition Improvement

Iris recognition has been improved considerably with the help of image recognition technology that recognizes the unique patterns in the iris. One of the most important and essential applications of iris recognition is biometric identification.


However, an innovative application of this would be its integration in high-end flagship smartphones; Samsung Galaxy Note 7, Galaxy S8, and Windows Lumia 950, to name a few. Unfortunately, smartphones’ security wasn’t guaranteed through iris recognition. Nonetheless, this technology in the works still has given way to the visualization of many new upcoming technologies that can rely on iris recognition.

These were just some of the futuristic implementations of image recognition – coming into 2020, there is better and more refined technology waiting to be incorporated into new avenues of tech-breakthroughs.

Text to speech

In essence, text to speech is the generation of synthesized speech from text. It was primarily designed as an assistive technology to help individuals with hearing impairments, visual and learning disabilities, and aged citizens to understand and consume content in a better manner. Today, the applications of TTS systems have grown manifold, and range from content creation to voiceover generation to customer service, and more. With a touch of a button, TTS can take words on a computer or other digital device and convert them into audio files. Today, the technology is used to create narratives for explainer videos or product demos, turn a book into an audio book. The audio enhancer is used to improve these audio files. Generate voiceovers for elearning materials, training videos, ads and commercials, YouTube videos, or podcasts, among other things.

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