Image Recognition Market Size, Share, Growth Analysis, By Technology(Digital Image Processing, Facial Recognition, Pattern Recognition), By Component(Hardware, Software, Services), By Deployment Mode(On-premises & Cloud), By Application(Augmented Reality, Scanning & Imaging, Security & Surveillance, Marketing & Advertising), By Region - Industry Forecast 2024-2031

Image Recognition Market Driver

• Processes for executing retail transactions that are profitable, efficient, and compliant are required.

The management of consumer contacts, product marketing both online and offline, and inventory control in retail spaces are all being revolutionised by image recognition technologies. More and more, internet shopping is being done using image recognition technologies. For instance, Snap, the parent company of Snapchat, is now creating a visual product feature for Snapchat that will make use of image recognition technology and enable users to capture images of objects in the real world as well as recognise, explore, and buy items on Amazon. Entrepreneurs and merchants have realised that traditional techniques of sales promotions, marketing, and visual merchandising will not be viable in the business in the long run as a result of the boom in the e-commerce sector, and much more so during the COVID-19 epidemic. In order to provide a superior consumer experience, merchants are swiftly adjusting to the new era of AI and picture recognition. The application of image recognition for shelf identification, product positioning, and upholding merchandising norms is rapidly gaining ground. Manufacturers, retailers, and marketers may better understand their markets and respond to them quickly thanks to image recognition technologies.

Image Recognition Market Restraint

• High cost of installing image recognition software

Making image recognition systems is expensive, which can limit the market's expansion. Face recognition, deep learning, computer vision, AI, ML, and gesture recognition are just a few examples of the enabling technologies that have expensive development costs. Therefore, even though they are interested in such solutions to boost productivity, businesses with limited financial resources do not choose picture recognition options. It is challenging for small businesses to use well-known vendor solutions since they are so expensive, like Microsoft Computer Vision API, Microsoft Emotion API, Amazon Recognition, Google Cloud Vision API, and IBM Watson Visual Recognition. Small retail and e-commerce enterprises would be discouraged by the high costs associated with adopting image recognition solutions and teaching AI enablers how to carry out a specific activity, which could prove to be a constraint for image recognition solution suppliers throughout the forecast period.