세계의 데이터 주석 시장 예측(-2032년) : 유형, 방법, 주석, 용도, 최종 사용자, 지역별 분석

According to Stratistics MRC, the Global Data Annotation Market is accounted for $2.74 billion in 2025 and is expected to reach $20.02 billion by 2032 growing at a CAGR of 32.8% during the forecast period. Data annotation is the process of labeling or tagging raw data such as text, images, audio, or video to make it understandable for machine learning models. It involves identifying relevant features, assigning metadata, and categorizing content to train algorithms for tasks like object recognition, sentiment analysis, or speech processing. Accurate annotation ensures model reliability and performance across applications. This foundational step is critical in supervised learning, enabling systems to interpret and respond to real-world inputs with precision and contextual awareness.

According to the study published in Analyzing Dataset Annotation Quality Management in the Wild 48% of reviewed publications employed labeling as their primary form of data annotation, while 31% utilized text production methods, highlighting the dominance of structured labeling in machine learning dataset preparation.

Market Dynamics:

Driver:

Rise of computer vision and natural language processing

Computer vision applications ranging from autonomous vehicles to facial recognition require precisely labeled image and video data to function accurately. Similarly, natural language processing (NLP) tools used in chatbots, sentiment analysis, and machine translation depend on annotated text to improve contextual understanding. As AI models become more sophisticated, the need for diverse, domain-specific annotations is intensifying, driving market expansion. The proliferation of edge AI and real-time analytics further amplifies the importance of scalable annotation solutions.

Restraint:

High cost and time-consumption of manual annotation

Manual data labeling remains a labor-intensive process, often requiring skilled annotators to spend hours tagging complex datasets. This not only increases operational costs but also slows down project timelines, especially for large-scale AI deployments. Industries such as healthcare and autonomous driving demand high precision, making manual annotation indispensable yet inefficient. Additionally, maintaining annotation consistency across teams and geographies poses a challenge, impacting model accuracy.

Opportunity:

Advancements in automated and semi-automated annotation tools

Semi-automated platforms leverage machine learning algorithms to suggest annotations, which are then verified or corrected by experts, significantly reducing turnaround time. These tools are increasingly integrated with cloud-based workflows, enabling remote collaboration and real-time updates. Moreover, the emergence of synthetic data generation and transfer learning is minimizing the need for extensive manual labeling. As annotation platforms become more intuitive and customizable, they are opening doors for broader adoption across SMEs and academic institutions.

Threat:

Regulatory uncertainty and changing compliance standards

Regulatory frameworks such as GDPR, HIPAA, and emerging AI-specific legislation are imposing stricter guidelines on how annotated data especially personal or biometric information is collected and processed. Companies must navigate evolving compliance landscapes, which vary across regions and sectors, adding complexity to cross-border operations. Failure to adhere to these standards can result in legal penalties and reputational damage. Additionally, ethical concerns around biased annotations and misuse of labeled data are prompting calls for transparency and accountability in annotation practices.

Covid-19 Impact:

The pandemic accelerated digital transformation across sectors, boosting demand for AI-driven solutions and, by extension, annotated datasets. With remote work becoming the norm, companies turned to cloud-based annotation platforms to maintain continuity in data labeling projects. Healthcare and retail industries saw a surge in AI applications from diagnostic imaging to contactless shopping requiring rapid annotation of new data types. However, initial disruptions in workforce availability and supply chains slowed down manual annotation efforts.

The image/video annotation segment is expected to be the largest during the forecast period

The image/video annotation segment is expected to account for the largest market share during the forecast period due to its critical role in enabling computer vision applications. From autonomous navigation systems to surveillance analytics, these annotations provide spatial and contextual cues essential for machine interpretation. The segment benefits from rising demand in sectors such as automotive, healthcare, and retail, where visual data is abundant and increasingly leveraged for decision-making.

The semantic annotation segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the semantic annotation segment is predicted to witness the highest growth rate driven by its pivotal role in enhancing NLP capabilities. By tagging entities, concepts, and relationships within text, semantic annotation enables machines to understand context, intent, and meaning more accurately. This is particularly valuable in applications like voice assistants, legal document analysis, and automated customer support. The segment is witnessing rapid growth due to the integration of knowledge graphs and ontologies, which improve annotation depth and relevance.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share attributed robust growth in AI adoption across emerging economies. Countries like China, India, and South Korea are investing heavily in AI infrastructure, creating substantial demand for annotated datasets. The region's large pool of skilled annotators and cost-effective labor makes it a hub for outsourcing annotation services. Additionally, government initiatives promoting digital transformation and smart city development are accelerating the deployment of AI solutions, thereby driving the need for scalable annotation platforms.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR fueled by technological innovation and strong enterprise adoption of AI. The region is home to leading annotation platform providers and research institutions that are continuously advancing annotation methodologies. Growing investments in autonomous vehicles, healthcare AI, and defense applications are generating demand for high-quality labeled data. Furthermore, the presence of stringent data privacy regulations is encouraging the development of secure and compliant annotation workflows.

Key players in the market

Some of the key players in Data Annotation Market include TELUS International, SuperAnnotate, Shaip, Scale AI, Playment, LightTag, Labelbox, Keylabs, iMerit, Hive, Defined.ai, Deepen AI, Cogito Tech, CloudFactory, Appen, Amazon Mechanical Turk, and Alegion.

Key Developments:

In July 2025, SuperAnnotate announced a strategic partnership with Fireworks AI, enabling rapid feedback loops for GenAI model validation. This collaboration allows customers to iterate on model tuning faster and more cost-effectively.

In March 2025, Shaip expanded its GenAI data capabilities, announcing a partnership with Protege to enhance access to high-quality healthcare data. This strengthens their position in delivering compliant and scalable medical datasets.

In March 2025, TELUS announced exploration of strategic financing moves to strengthen its wireless infrastructure, including evaluating the sale of a minority stake in its tower assets. This aligns with their ongoing efforts to supercharge next-gen wireless networks while optimizing balance sheet.

Types Covered:

• Image/Video Annotation
• Text Annotation
• Audio Annotation
• Other Types

Methods Covered:

• Manual Annotation
• Semi-Supervised/Hybrid Annotation
• Automated Annotation

Annotations Covered:

• Semantic Annotation
• Instance Annotation
• Polygon Annotation
• Bounding Box Annotation
• Keypoint Annotation
• Other Annotations

Applications Covered:

• Computer Vision
• Natural Language Processing (NLP)
• Healthcare Diagnostics
• Robotics
• Speech Recognition
• E-commerce Personalization
• Autonomous Vehicles
• Other Applications

End Users Covered:

• IT & Telecom
• Government & Defense
• BFSI
• Retail & E-commerce
• Media & Entertainment
• Other End Users

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Application Analysis
• 3.7 End User Analysis
• 3.8 Emerging Markets
• 3.9 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global Data Annotation Market, By Type

• 5.1 Introduction
• 5.2 Image/Video Annotation
  • 5.2.1 Bounding Box
  • 5.2.2 Polygons & Lines
  • 5.2.3 3D Annotation/LiDAR

5.2.4 Semantic Segmentation:

• 5.2.5 Keypoint Annotation
• 5.3 Text Annotation
  • 5.3.1 Named Entity Recognition (NER)
  • 5.3.2 Sentiment Analysis
  • 5.3.3 Document & Content Categorization
• 5.4 Audio Annotation
  • 5.4.1 Speech Transcription
  • 5.4.2 Sound Event Detection
  • 5.4.3 Speaker Identification
• 5.5 Other Types

6 Global Data Annotation Market, By Method

• 6.1 Introduction
• 6.2 Manual Annotation
• 6.3 Semi-Supervised/Hybrid Annotation
• 6.4 Automated Annotation

7 Global Data Annotation Market, By Annotation

• 7.1 Introduction
• 7.2 Semantic Annotation
• 7.3 Instance Annotation
• 7.4 Polygon Annotation
• 7.5 Bounding Box Annotation
• 7.6 Keypoint Annotation
• 7.7 Other Annotations

8 Global Data Annotation Market, By Application

• 8.1 Introduction
• 8.2 Computer Vision
• 8.3 Natural Language Processing (NLP)
• 8.4 Healthcare Diagnostics
• 8.5 Robotics
• 8.6 Speech Recognition
• 8.7 E-commerce Personalization
• 8.8 Autonomous Vehicles
• 8.9 Other Applications

9 Global Data Annotation Market, By End User

• 9.1 Introduction
• 9.2 IT & Telecom
• 9.3 Government & Defense
• 9.4 BFSI
• 9.5 Retail & E-commerce
• 9.6 Media & Entertainment
• 9.7 Other End Users

10 Global Data Annotation Market, By Geography

• 10.1 Introduction
• 10.2 North America
  • 10.2.1 US
  • 10.2.2 Canada
  • 10.2.3 Mexico
• 10.3 Europe
  • 10.3.1 Germany
  • 10.3.2 UK
  • 10.3.3 Italy
  • 10.3.4 France
  • 10.3.5 Spain
  • 10.3.6 Rest of Europe
• 10.4 Asia Pacific
  • 10.4.1 Japan
  • 10.4.2 China
  • 10.4.3 India
  • 10.4.4 Australia
  • 10.4.5 New Zealand
  • 10.4.6 South Korea
  • 10.4.7 Rest of Asia Pacific
• 10.5 South America
  • 10.5.1 Argentina
  • 10.5.2 Brazil
  • 10.5.3 Chile
  • 10.5.4 Rest of South America
• 10.6 Middle East & Africa
  • 10.6.1 Saudi Arabia
  • 10.6.2 UAE
  • 10.6.3 Qatar
  • 10.6.4 South Africa
  • 10.6.5 Rest of Middle East & Africa

11 Key Developments

• 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 11.2 Acquisitions & Mergers
• 11.3 New Product Launch
• 11.4 Expansions
• 11.5 Other Key Strategies

12 Company Profiling

• 12.1 TELUS International
• 12.2 SuperAnnotate
• 12.3 Shaip
• 12.4 Scale AI
• 12.5 Playment
• 12.6 LightTag
• 12.7 Labelbox
• 12.8 Keylabs
• 12.9 iMerit
• 12.10 Hive
• 12.11 Defined.ai
• 12.12 Deepen AI
• 12.13 Cogito Tech
• 12.14 CloudFactory
• 12.15 Appen
• 12.16 Amazon Mechanical Turk
• 12.17 Alegion