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Global Image Recognition in Retail Market (2020-2025), by Technology, Component, Application, Deployment, Geography and the Impact of Covid-19 with Ansoff Analysis

  • ID: 5206802
  • Report
  • December 2020
  • Region: Global
  • 145 pages
  • Infogence Marketing and Advisory Services
The Global Image Recognition in Retail Market is Estimated to be USD 1.5 Bn in 2020 and is Expected to Reach USD 3.7 Bn by 2025, Growing at a CAGR of 20%


  • Amazon
  • Blippar
  • Hitachi, Ltd.
  • Intelligent Retail
  • Microsoft Corporation
  • Planorama

Image Recognition means identifying a specific image and placing it in a predetermined category. It uses computer algorithms for digital image processing and thus helps in processing videos and removing blur images. The technology further allows images to convert into two or more defined dimensions, which categorize the digital image processing as multidimensional systems. Image recognition in retail includes the technologies which help to enhance the in-store experiences of customers. The use of high bandwidth data services in the retail and BFSI sector can be attributed to the growth of the image recognition market. android devices with cameras are attracting vendors to invest in the market. Increasing demand for security in products and applications is also influencing the growth of the image recognition market.

Large enterprises in different sectors, such as retail, automotive, healthcare, and defense, are increasingly adopting image recognition technology. Several other fields, such as self-driving vehicles, automated image organization of visual websites, and face identification on social networking websites, are using the Image recognition technology powered by machine learning. Government agencies such as law enforcement agencies are also using facial recognition technology for their safety and security purposes. Airports are also using face remembrance technology at security checkpoints for security purposes. Recent advancements in artificial intelligence and machine learning have highly contributed to the growth of Image Recognition and Object Detection in retail.

The Global Image Recognition in Retail Market is estimated to be USD 1.5 Bn in 2020 and is expected to reach USD 3.7 Bn by 2025, growing at a CAGR of 20%.

Certain factors may create hindrances in the growth of image recognition in the retail market, such as the high cost involved in making the image recognition systems, lack of technical skills, etc. Thus, companies who lack resources are unable to adopt this technology even if they are interested in image recognition. On the other hand, a huge number of social media applications are available on the internet, and a good amount of the population is daily uploading a billion images per day on social media platforms such as Facebook, WhatsApp, Snapchat, Instagram, etc. which helps in increasing the adoption of image recognition and will drive the growth of the Global Image Recognition in Retail Market.

Market Dynamics

  • Need to Increase On-Shelf Availability, Enhance Customer Experience, and Maximize ROI
  • Increasing the Use of Image Recognition Applications
  • Increasing Use of High Bandwidth Data Services
  • Increasing Demand for Security Applications and Products Enabled with Image Recognition Functions
  • High Risk Related to Customer Data Thefts
  • High Cost of Installation of Image Recognition Systems
  • Growing Adoption of Cloud-Based Image Recognition Solutions
  • Rising Demand for Brand Recognition Among End Users
  • Increasing Demand for Big Data Analytics
Segments Covered
Based on Technology, Barcode Recognition is forecasted to grow rapidly in the current market scenario. This is one of the significant image identification techniques that many corporations rapidly adopt to track their fixed assets. There are several benefits of barcode recognition which have encouraged businesses to adopt barcode scanners, such as smooth internal operations, time-saving, and accuracy. Also, the use of barcode recognition in various applications, such as entertainment, advertisement, games, and tracking products, has contributed a significant market share of this technique. Retail businesses are highly adopting this technique, and this will help in boosting the growth of the barcode recognition segment in the forecasted period.

Based on Component, the Service segment of the market is expected to grow at the highest CAGR. The service segment forms an important part of the image recognition in the retail software lifecycle. It includes implementation, deployment, consulting, product upgrades, and maintenance. The services segment is categorized into many parts such as professional services, managed services, and training, support & maintenance services. These services will help the organizations to adopt new image recognition software in the market.

Based on Application, Marketing and Advertising segment dominates the market. A large number of enterprises and businesses are adopting image recognition technology to improve their marketing activities with advanced advertising, branding, and customer interaction. Social media platforms are also using AI-enabled image recognition technologies to improve their user experience and to allow advertisers to place relevant advertisements.

Based on Deployment Type, the Cloud segment is forecasted to grow at a higher CAGR during the forecast period. High adoption of innovative technologies, such as social media, web, mobile, online applications, and the increasing use of the internet helps in the growth of cloud-based image recognition techniques. Retailers are adopting cloud-based image recognition software solutions on-premises to encourage and strengthen marketing endeavors, as they do not involve high upfront costs. Cloud-based services reduce IT staff, licensing costs, and offer retailers greater flexibility to expand their business.

Based on Geography, North America is estimated to hold the largest market share in the Global Image Recognition in Retail Market during the forecast period. North America has always been an open market in terms of adopting new and advanced technologies across all industries. These regions are the most progressive in adopting Artificial Intelligence (AI), Machine Learning (ML), and cloud technologies. There has been a high adoption of technologies over cloud and mobile for streamlining work processes.

The Global Image Recognition in Retail Market is segmented based on Technology, Component, Application, Deployment, and Geography.

Global Image Recognition in Retail Market, By Technology
  • Introduction
  • Barcode Recognition
  • Digital Image Processing
  • Object Recognition
  • Facial Recognition
  • Other Technologies
Global Image Recognition in Retail Market, By Component
  • Introduction
  • Services
  • Software
  • Hardware
Global Image Recognition in Retail Market, By Application
  • Introduction
  • Visual Product Search
  • Marketing and Advertising
  • Security and Surveillance
  • Vision Analytics
  • Other Applications
Global Image Recognition in Retail Market, By Deployment
  • Introduction
  • Cloud
  • On-Premises
Global Image Recognition in Retail Market, By Geography
  • Introduction
  • North America
  • South America
  • Europe
  • Asia Pacific
  • The Middle East and Africa
Company Profiles

Some of the companies covered in this report are Amazon AWS, Google LLC, IBM Corporation, Microsoft Corporation, TRAX Retail, Qualcomm Technologies, Inc., NEC Corporation, LTU Technologies, Catchoom Technologies S.L., Honeywell International Inc., Hitachi, Ltd., Slyce Inc., Wikitude GmbH, Attrasoft, Inc., Planorama, Ricoh Innovations Corporation, Pattern Recognition Company GMBH, Intelligent Retail, Snap2Insight Inc. and Blippar.

Competitive Quadrant

The report includes Competitive Quadrant, a proprietary tool to analyze and evaluate the position of companies based on their Industry Position score and Market Performance Score. The tool uses various factors for categorizing the players into four categories. Some of these factors considered for analysis are financial performance over the last 3 years, growth strategies, innovation score, new product launches, investments, growth in market share, etc.

Lions: Represents companies with a strong foothold in the market, with the highest market share, large investments in technologies, new products.
Bulls: Companies that are medium in size competing with their USPs, growing companies with proven market share.
Rabbits: Small companies but growing rapidly, constantly improving their offerings in the market.
Tortoise: Companies which are slow in growth, having a long legacy, and stable or negative in performance.

Why Buy this report?
  • The report offers a comprehensive evaluation of Global Image Recognition in Retail Market. - The report includes in-depth qualitative analysis, verifiable data from authentic sources, and projections about market size. The projections are calculated using proven research methodologies.
  • The report has been compiled through extensive primary and secondary research. The primary research is done through interviews, surveys, and observation of renowned personnel in the industry.
  • The report includes in-depth market analysis using Porter’s 5 force model and the Ansoff Matrix. The impact of Covid-19 on the market is also featured in the report.
  • The report also contains the competitive analysis using Competitive Quadrant, Infogence’s Proprietary competitive positioning tool.
Report Highlights:
  • A complete analysis of the market including parent industry
  • Important market dynamics and trends
  • Market segmentation
  • Historical, current, and projected size of the market based on value and volume
  • Market shares and strategies of key players
  • Recommendations to companies for strengthening their foothold in the market
Note: Product cover images may vary from those shown


  • Amazon
  • Blippar
  • Hitachi, Ltd.
  • Intelligent Retail
  • Microsoft Corporation
  • Planorama
1. Report Description
1.1 Study Objectives
1.2 Market Definition
1.3 Currency
1.4 Years Considered
1.5 Language
1.6 Key Shareholders

2. Research Methodology
2.1 Research Process
2.2 Data Collection and Validation
2.2.1 Secondary Research
2.2.2 Primary Research
2.3 Market Size Estimation
2.4 Assumptions of the Study
2.5 Limitations of the Study

3. Executive Summary

4. Market Overview
4.1 Introduction
4.2 Market Dynamics
4.2.1 Drivers
4.2.2 Restraints
4.2.3 Opportunities
4.2.4 Challenges
4.3 Trends

5. Market Analysis
5.1 Porter’s Five Forces Analysis
5.2 Impact of COVID-19
5.3 Ansoff Matrix Analysis
5.4 SWOT Analysis

6. Global Image Recognition in Retail Market, By Technology
6.1 Introduction
6.2 Barcode Recognition
6.3 Digital Image Processing
6.4 Object Recognition
6.5 Facial Recognition
6.6 Other Technologies

7. Global Image Recognition in Retail Market, By Component
7.1 Introduction
7.2 Services
7.3 Software
7.4 Hardware

8. Global Image Recognition in Retail Market, By Application
8.1 Introduction
8.2 Visual Product Search
8.3 Marketing and Advertising
8.4 Security and Surveillance
8.5 Vision Analytics
8.6 Other Applications

9. Global Image Recognition in Retail Market, By Deployment
9.1 Introduction
9.2 Cloud
9.3 On-Premises

10. Global Image Recognition in Retail Market, By Geography
10.1 Introduction
10.2 North America
10.2.1 US
10.2.2 Canada
10.2.3 Mexico
10.3 South America
10.3.1 Brazil
10.3.2 Argentina
10.4 Europe
10.4.1 UK
10.4.2 France
10.4.3 Germany
10.4.4 Italy
10.4.5 Rest of Europe
10.5 Asia-Pacific
10.5.1 China
10.5.2 Japan
10.5.3 India
10.5.4 Australia
10.5.5 Rest of APAC
10.6 Middle East and Africa

11. Competitive Landscape
11.1 Competitive Quadrant
11.2 Market Share Analysis
11.3 Competitive Scenario
11.3.1 Mergers & Acquisitions
11.3.2 Agreements, Collaborations, & Partnerships
11.3.3 New Product Launches & Enhancements
11.3.4 Investments & Fundings

12. Company Profiles
12.1 Amazon
12.2 Google LLC
12.3 IBM Corporation
12.4 Microsoft Corporation
12.5 TRAX Retail
12.6 Qualcomm Technologies, Inc.
12.7 NEC Corporation
12.8 LTU Technologies
12.9 Catchoom Technologies S.L.
12.10 Honeywell International Inc.
12.11 Hitachi, Ltd.
12.12 Slyce Inc.
12.13 Wikitude GmbH
12.14 Attrasoft, Inc.
12.15 Planorama
12.16 Ricoh Innovations Corporation
12.17 Pattern Recognition Company GMBH
12.18 Intelligent Retail
12.19 Snap2Insight Inc.
12.20 Blippar

13. Appendix
13.1 Questionnaire
Note: Product cover images may vary from those shown
  • Amazon
  • Google LLC
  • IBM Corporation
  • Microsoft Corporation
  • TRAX Retail
  • Qualcomm Technologies, Inc.
  • NEC Corporation
  • LTU Technologies
  • Catchoom Technologies S.L.
  • Honeywell International Inc.
  • Hitachi, Ltd.
  • Slyce Inc.
  • Wikitude GmbH
  • Attrasoft, Inc.
  • Planorama
  • Ricoh Innovations Corporation
  • Pattern Recognition Company GMBH
  • Intelligent Retail
  • Snap2Insight Inc.
  • Blippar
Note: Product cover images may vary from those shown