The global image recognition market size was valued at USD 27.3 billion in 2019 and is expected to register a CAGR of 18.8% from 2020 to 2027. Image recognition technology, powered by machine learning, has been embedded in several fields, such as self-driving vehicles, automated image organization of visual websites, and face identification on social networking websites. One of the most popular applications of image identification is social media monitoring, as visual listening and visual analytics are the essential factors of digital marketing. Also, this technology is highly used in applications related to safety and security, such as facial recognition used by law enforcement agencies. Furthermore, airports are increasingly using face remembrance technology at security checkpoints. For instance, in August 2019, Transportation Security Administration (TSA) conducted a short-term test for identity verification automation of the Travel Document Checker (TDC) at McCarran International Airport (LAS). This test involved the facial recognition technology to assess the TDC’s ability to compare an image taken from passenger’s identity document against the passenger’s photo taken at the checkpoint.
Major companies from various verticals, such as automotive, retail and e-commerce, security, and healthcare, are rapidly implementing digital image processing. For instance, in July 2019, Trax, a global provider of solutions in analytics and computer vision for retail, acquired the Planorama, which is a Europe-based image identification services provider for merchandising and retail execution. This acquisition was made to serve consumer packaged goods (CPG) customers globally by providing a comprehensive set of in-store execution solutions. Since Planorama offers a wide range of CPG-centric solutions to provide merchandising recommendations, by leveraging deep learning-based image processing, Trax would be able to expand its business in analytics services powered by machine learning platforms.
With the advent of cloud media services and surge in mobile devices, numerous image identification technologies have emerged, such as content moderation, visual search, and face remembrance. Face remembrance is being widely used in law enforcement and government applications. It has gained more popularity in commercial applications for use cases, such as access control through biometrics and digital payments. For instance, in July 2019, the Romanian Protection and Guard Service implemented the NeoFace facial recognition engine, offered by NEC Corporation, for access control at the EU Summit in Romania. Furthermore, tech giants such as NEC Corporation are working on bringing body-recognition systems by the end of the year 2020. This technology will be able to re-identify people during a single visit to a place, such as an airport or stadium.
The driving force behind the recent advancements in image recognition is machine learning and artificial intelligence capabilities. Deep neural networks have been designed for a variety of figure identification-related tasks, which have greatly surpassed traditional methods based on hand-crafted image features. For instance, in April 2019, Honeywell International Inc., launched its new security platform, MAXPRO Video Management Systems and Network Video Recorders, in collaboration with Intel Corporation, the tech giant in the field of semiconductors. Intel Vision products powered by deep learning techniques have been incorporated in MAXPRO, to enable face remembrance capabilities. Advances in security and surveillance have increased the demand for high-definition identification techniques such as edge video analytics and security.
The increasing preference among individuals for high bandwidth data services and advanced machine learning has led to the increased demand for image recognition technology. Establishments among various verticals such as media & entertainment; retail; IT & telecom; and Banking, Financial Services, and Insurance (BFSI) have resulted in the increasing use of advanced technologies in their organization, which, in turn, has boosted the adoption of image recognition. The image recognition system helps to identify objects, buildings, places, logos, and people, among other images. Furthermore, recent developments in the image recognition technology have allowed users to link offline content, such as brochures and magazines, with digital content such as promotional videos, augmented reality experiences, and product information with the help of image captured on a smartphone.
Furthermore, automated image recognition system plays a crucial role in computer vision, which is used to identify or detect an image or an attribute in digital images or videos. It enables users to gather and analyze data in real-time. The data is collected in high dimension and produces numerical or symbolic information. As part of image recognition, computer vision enables object recognition, event detection, image reconstruction, learning, and video tracking.
The image recognition technology has witnessed several opportunities emerging in applications such as big data analytics and effective branding of products and services, owing to the extending reach of image database. Some of these image databases, such as ImageNet and Pascal VOC, are freely available. The database contains millions of keyword-tagged images that describe the objects present in the image. It forms the basis for image recognition and enables computers to identify the objects accurately and quickly in the picture. For instance, image recognition solutions quickly identify dogs in the image because it has learned what dogs look like by analyzing numerous images tagged with the word “dog”. Some of the leading social networking sites, such as Google and Facebook, have the advantage of accessing several user-labeled photos directly from their database to prepare their deep-learning networks in order to be highly accurate in detecting objects.
Furthermore, since database serves as the training material to image recognition solutions, open-source frameworks such as software libraries and software tools form the building blocks of the solution. It helps to prepare or train machines to learn from the images available in the database by providing different types of computer vision functions such as medical screening, obstacle detection in vehicles, and emotion & facial recognition, among others. Some of the leading libraries for image recognition include UC Berkeley's Caffe, Google TensorFlow, and Torch.
Based on the technique, the market has been segmented into object recognition, QR/ barcode recognition, pattern recognition, facial recognition, and optical character recognition. The object recognition segment held a significant market share in 2019. Object identification is a form of computer vision that has gained momentum in both the consumer-facing tech companies and enterprises. Also, facial recognition is expected to demonstrate a notable shift in its growth over the forecast period as it is being adopted in industries ranging from manufacturing to security and surveillance.
QR/barcode recognition is also one of the significant image identification techniques as barcode scanners are rapidly adopted by corporations to track their fixed assets. Several benefits of barcode recognition, such as smooth internal operations, time-saving, and accuracy, have encouraged businesses to adopt barcode scanners. Also, the use of barcode recognition in numerous applications, such as entertainment, advertisement, games, art and pop culture, and tracking products, has contributed to the significant market share of this technique. The adoption of this technique in retail and other businesses is expected to boost the growth of the QR/ barcode recognition segment in the coming years.
Based on applications, the market has been segmented into scanning and imaging, security and surveillance, augmented reality, marketing and advertising, and image search. The marketing and advertising segment dominated the market in 2019 as many businesses adopted the technology to improve their marketing activities with advanced advertising, customer interaction, and branding. The major social media platforms are using AI-enabled image recognition technologies to improve the user experience and allow advertisers to place contextually relevant advertisements. For instance, in November 2018, Slyce Acquisition Inc., a service provider in visual search for retailers, acquired the intellectual property assets of Ditto Labs, the developer of image recognition software for social media monitoring. This acquisition was made to provide great insights about product usage and sharing across the internet.
The augmented reality segment is anticipated to witness substantial growth and is projected to expand at a healthy CAGR over the forecast period. The image identification technology can detect 2D images and trigger augmented content to appear in the form of slideshows, videos, sound, 360° panoramas, 3D animations, and text. Image recognition in augmented reality is being used for multiple purposes, such as product display, entertainment, and augmentation of printed magazines.
For instance, Busch-Jaeger Elektro GmbH, a global provider of electrical installation technology and related products and services, uses augmented reality to create product presentations. Furthermore, abenteuer und reisen, a Germany-based travel magazine based on city trips, long-distance travel, and lifestyle, has a substantial amount of its app-users accessing augmented reality experiences within its printed editions.
Based on components, the image recognition market has been segmented into hardware, software, and service. The service segment is anticipated to witness a noticeable growth rate over the forecast period. The software segment held a significant market share in 2019 owing to the growing adoption of image processing software for various applications, such as medical imaging, computer graphics, and photo editing.
The trend of using digital image processing in the development of computer vision-related software with the help of API is expected to enhance the demand for the market over the forecast period. For instance, several API platforms, such as Amazon Rekognition, provide an option of adding video or image analytics to an application and enhance the object identification capability of the application. Also, Microsoft Computer Vision API, a cloud-based API tool offered by Microsoft, provides developers with access to image processing and content visualization. Several businesses and corporations leverage the mage recognition software for boosting content discoverability and accelerating text extraction.
Based on the deployment-mode, the market has been segmented into on-premise and cloud. The cloud segment dominated the market in 2019 and is anticipated to retain its position over the forecast period. This growth in the cloud-based market is attributed to its increased adoption in verticals where centralized monitoring is required, such as Banking, Financial Services, and Insurance (BFSI), media and entertainment, and government. Also, cloud-based deployment provides access to the API (Application Programming Interface) available in different servers or sources.
Several organizations are implementing cloud-based image processing solutions to secure confidential data and reinforce their marketing endeavors. These solutions assist users in adopting innovative techniques in their daily activities without hampering their budgets. For instance, Amazon Rekognition powered by deep learning technology gives highly accurate facial search capabilities and facial analysis that can be used for detection, analyzing, and comparing faces for a wide variety of user verification and public safety use cases. Also, Google Vision, the part of Google Cloud Platform, enables developers to implement machine learning models to understand the content of the image.
Based on vertical, the market has been segmented into media and entertainment, BFSI, automobile and transportation, retail and e-commerce, telecom and IT, government, healthcare, and others. The media and entertainment segment represented a significant share in 2019 owing to the capability of the image recognition technology to extract business insights from pictures shared on social media. The evolution of virtual reality environments has transformed the media and entertainment industry, including interactive media, games, advertising, and TV and film production experience, by implementing computer vision and machine learning. For instance, Watson Media, offered by IBM Corporation, allows sports enthusiasts to capture highlights of a match automatically and share them on social media.
Besides, the retail and e-commerce segment dominated the market in 2019 and is expected to remain the dominant vertical segment throughout the forecast period. With the help of image identification, online shoppers can search for clothing or accessories by taking a picture of a garment, texture, print, or color of their choice. The photo captured by the smartphone is uploaded to an app that searches an inventory of products to find similar products using AI technology. Also, image recognition technology is being increasingly adopted in autonomous vehicles, which is anticipated to contribute to the noticeable growth in the automobile and transportation segment. Autonomous cars can detect obstacles and warn the driver about the proximity to walkways and guardrails with the help of this technology. The technology is also capable of reading stoplights and road signs.
North America accounted for the largest market share in 2019, majorly due to rapid growth of cloud-based streaming services in the U.S. The growth of the segment is attributed to the increasing integration of artificial intelligence and mobile computing platforms in the field of digital shopping and e-commerce. The European regional market is expected to witness significant growth over the forecast period owing to growing advancements in automobile obstacle detection technologies in the region.
The image recognition market is expected to witness a surge in the adoption of the technology across various regions owing to benefits such as extracting business insights from socially shared pictures and auto-organizing untagged photo collections. In addition, it also offers safety features in autonomous cars. The rising competition among image recognition solution providers has propelled vendors to focus on the development of innovative products to sustain in the competition.
On the other hand, Asia Pacific is projected to demonstrate growth at the highest CAGR over the forecast period. This growth is attributed to increasing use of mobiles and tablets, rapid technological advancements, and the popularity of online streaming in emerging economies, such as China and India. The growth is attributed to the high adoption of mobility and cloud solutions to address information security. Factors such as the economic growth of countries like China and India, the increasing adoption of smartphones, and developing e-commerce sector are fueling the market growth.
The growing applications of face remembrance in security and surveillance systems in China are projected to drive market growth in the Asia Pacific region. For instance, the Chinese government has enforced real-name registration policies in the country, under which citizens are required to link their online account with the official government ID. These policies have made the use of image recognition more ubiquitous across the nation.
The key industry participants in the market include Attrasoft, Inc.; Google; Catchroom; Hitachi, Ltd.; Honeywell International Inc; LTUTech; NEC Corporation; Qualcomm Technologies, Inc.; Slyce Acquisition Inc.; and Wikitude GmbH. Vendors in the market are focusing on increasing the customer base to gain a competitive edge in the market. Therefore, vendors are taking several strategic initiatives, such as enhancing their products by adding new features, collaborations, acquisitions and mergers, and partnerships with other key players in the market. For instance, in March 2018, Microsoft launched its pre-built tools with updated services, namely Face API, Custom Vision Service, and Bing Entity Search. The updates in these services involve improvement in custom image classification and facial recognition. Also, in May 2019, Wikitude GmbH launched the Wikitude SDK 8.5 with new image recognition features, such as transparent area feature and image target collection. Such enhancement in services/products/solutions made by the companies are strategic initiatives to compete against other market players.
Base year for estimation
Actual estimates/Historical data
2016 - 2018
2020 - 2027
Revenue in USD Million and CAGR from 2020 to 2027
North America, Europe, Asia Pacific, South America, MEA
U.S., Canada, Mexico, Germany, U.K., China, Japan, India, and Brazil
Revenue forecast, company share, competitive landscape, growth factors, and trends
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This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2016 to 2027. For this study, Grand View Research has segmented the global image recognition market report based on technique, application, component, deployment mode, vertical, and region.
Technique Outlook (Revenue, USD Million, 2016 - 2027)
QR/ Barcode Recognition
Optical Character Recognition
Application Outlook (Revenue, USD Million, 2016 - 2027)
Scanning & Imaging
Security & Surveillance
Marketing & Advertising
Component Outlook (Revenue, USD Million, 2016 - 2027)
Deployment Mode Outlook (Revenue, USD Million, 2016 - 2027)
Vertical Outlook (Revenue, USD Million, 2016 - 2027)
Retail & E-commerce
Media & Entertainment
Automobile & Transportation
IT & Telecom
Regional Outlook (Revenue, USD Million, 2016 - 2027)
Middle East and Africa (MEA)
b. The global image recognition market size was estimated at USD 27,294.2 million in 2019 and is expected to reach USD 32,693.0 million in 2020.
b. The global image recognition market is expected to grow at a compound annual growth rate of 18.8% from 2020 to 2027 to reach USD 1,09,438.5 million by 2027.
b. North America dominated the image recognition market with a share of 37.3% in 2019. This is attributable to the growing integration of artificial intelligence and mobile computing platforms in the field of digital shopping and e-commerce, in the region.
b. Some key players operating in the image recognition market include Attrasoft, Inc.; Google; Catchroom; Hitachi, Ltd.; Honeywell International Inc; LTUTech; NEC Corporation; Qualcomm Technologies, Inc.; Slyce Acquisition Inc.; and Wikitude GmbH.
b. Key factors that are driving the market growth include extended reach of image database and open source frameworks and growing adoption of image recognition in different verticals.
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Artificial Intelligence (AI), Virtual Reality (VR), and Augmented Reality (AR) solutions are anticipated to substantially contribute while responding to the COVID-19 pandemic and address continuously evolving challenges. The existing situation owing to the outbreak of the epidemic will inspire pharmaceutical vendors and healthcare establishments to improve their R&D investments in AI, acting as a core technology for enabling various initiatives. The insurance industry is expected to confront the pressure associated with cost-efficiency. Usage of AI can help in reducing operating costs, and at the same time, can increase customer satisfaction during the renewal process, claims, and other services. VR/AR can assist in e-learning, for which the demand will surge owing to the closure of many schools and universities. Further, VR/AR can also prove to be a valuable solution in providing remote assistance as it can support in avoiding unnecessary travel. The report will account for Covid19 as a key market contributor.