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    Computer Vision Mobile Apps: Examples, Features, and Development Tools

    Computer vision mobile apps offer incredible potential. With high flexibility and accuracy, they gain popularity in healthcare, banking, retail, agriculture, manufacturing, insurance, education sectors, and more. The power of this tool makes life easier, so it’s a great field for business development.

    At Surf, we have cross-industrial experience in creating computer vision apps. For 12 years, we have built products for companies such as KFC and SAP. In this article, we’ll discuss how CV technology revolutionizes businesses and help you choose trustworthy tools for your apps.

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    What is a computer vision mobile app today?

    Computer vision mobile applications enable users to interact with the digital world in completely new ways. By using cameras and specialized software, computer vision apps can detect and recognize objects, text, and even classify any item. 

    This technology is incredibly useful for a variety of tasks ranging from helping drivers navigate safer to providing detailed analysis of a person’s movements and creating smarter security systems. 

    How computer vision is used for various business purposes

    Let’s find out how applications of computer vision are used in various niches and learn more about benefits the technology offers to business.  

    Healthcare 

    Computer vision technology is revolutionizing healthcare. By utilizing sophisticated algorithms, these applications are able to process medical images and provide more accurate diagnoses in a fraction of the time it would take traditional methods. 

    Patients get care faster, doctors spend less time on tedious manual work, and the industry is becoming more efficient and cost-effective by 

    • creating a visualization of particular organs and tissues to enable a more accurate diagnosis and higher accuracy in decision-making,
    • reducing the chances of medical errors to increase patient safety, 
    • early detection of brain tumors and cancer cells and even differentiating between cancerous skin lesions and non-cancerous lesions,
    • offering aid in remote monitoring of patients in a non-intrusive manner,
    • training and assessing surgical skills with simulation-based surgical platforms for better understanding patients’ condition before operating on them,
    • diagnosing, controlling, treating, and prevention of COVID with digital chest X-ray radiography images,
    • queue detection, occupancy analysis, and people counting to reduce waits.

    As the healthcare industry continues to evolve, computer vision technology will be at the forefront of this transformation. 

    At Surf, we enthusiastically move forward to the future with computer vision. Now our developer team is working on an app to make life with diabetes easier and longer. The application helps patients control their disease by continuously monitoring their sugar level, TIR (Time In Range), and other important metrics.    

    To implement one of the features, food recognition, our ML and AI experts have used deep learning to develop a technology recognizing and marking each food item to count its nutrition value and help a person make up a healthy ration

    Get inspired by the best practices we implement for the clients’ projects
    Read the case studies

    Banking

    Computer vision technology is widely used in banking. With apps of this type, banks can gain valuable insights into their customers’ needs and habits and create more personalized services. And it is the least of what becomes possible for them with this technology. 

    Thanks to CV, banks 

    • identify abnormal behaviors and potential fraud to stop it immediately, 
    • use facial recognition, facial comparison, and live body detection for ATMs security, 
    • transform password verification for the clients,
    • automate the input of credit card application forms,
    • digitize paper documents into a format ready for validation and processing by using optical character recognition (OCR),
    • extract data from physical documents and organize data points to be stored in appropriate databases, 
    • automate labor-intensive tasks such as document verification, reducing costs and increasing operational efficiency.

    For example, Finansbank increased their daily credit card application processing from 1,000 to 10,000 applications processed by using computer vision technologies, OCR. In addition, the bank got an opportunity to cut the number of human operators by 87.5%.

    OCR (optical character recognition) used to automate labor-intensive tasks. Source: neosalpha.com

    Retail

    Computer vision apps in retail have become a game-changer for businesses. By leveraging AI-powered algorithms and machine learning, retailers can

    • detect shoplifters and alert staff, eliminating the need for costly security systems,
    • streamline operations and provide customers with unparalleled service,
    • track inventory levels in real-time, 
    • run predictive analytics based on purchasing patterns and better allocate resources,
    • identify faces of customers to improve personalization,
    • detect changes in customer preferences to better target their marketing efforts,
    • automate manual tasks associated with managing inventory and customer transactions and quickly identify stocked items.

    All the benefits above help retailers stay competitive and offer their customers what they need, reducing spending on routine tasks. 

    Restaurant business

    The technology is revolutionizing the restaurant business. By allowing restaurants to automate tedious tasks such as menu engineering, inventory tracking, and customer analytics, the applications can provide essential insights that help restaurants make informed decisions. 

    With a computer vision application, restaurant owners 

    • optimize menu offerings and inventory levels based on sales performance data, 
    • track customer visits to improve marketing and loyalty programs,
    • identify trends in food preferences and automate menu engineering, 
    • detect food safety incidents in real-time, 
    • scrutinize if workers are preparing orders correctly by installing overhead cameras in restaurant kitchens,
    • control personal protective equipment and hygiene standards,
    • analyze video streams to determine when and why potential customers abandon the drive-thru and minimize wait times. 

    Thanks to computer vision technologies, restaurant owners get prepared for high-volume periods and proactively set customer expectations to make data-driven decisions and stay ahead of the competition.

    At Surf, we have seen how it works while developing financial analysis and process management systems for KFC restaurants. The Client addressed us to develop a facial recognition system and automate working hours recording. 

    We created a system able to accurately identify restaurant employees, register the actual clock-ins and clock-outs, and send the data to the server. For implementing facial recognition in a mobile application, we used Face SDK library. 

    Computer vision app development tools

    In the picture below, you can see standard tools used in computer vision application development and their functionality. 

    For example, the Surf developer team used TensorFlow as one of the main tools to implement food recognition functionality in the diabetes monitoring mobile app, mentioned in the beginning of this article.

    Wrapping up

    Mobile computer vision possibilities are limited only by the human imagination. To explore how it can help your business be a step ahead of competitors, it’s enough to choose an experienced software developer team and start your ML-journey.