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商品編碼

1803041

全球資料註釋市場預測（至 2032 年）：按類型、方法、註釋、應用、最終用戶和地區進行分析

Data Annotation Market Forecasts to 2032 - Global Analysis By Type, Method, Annotation, Application, End User and By Geography

出版日期: 2025年09月07日 | 出版商:

Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

簡介目錄圖表

根據 Stratistics MRC 的數據，全球數據註釋市場預計在 2025 年達到 27.4 億美元，到 2032 年將達到 200.2 億美元，預測期內的複合年成長率為 32.8%。

資料標註是標記原始資料（例如文字、圖像、音訊或影片）的過程，以便機器學習模型能夠理解這些資料。它涉及識別相關特徵、分配元資料以及對內容進行分類，以訓練用於物件識別、情緒分析和語音處理等任務的演算法。準確的標註可確保模型在各種應用中的可靠性和性能。這一基本步驟在監督學習中至關重要，它使系統能夠準確地解讀現實世界的輸入並響應與上下文相關的輸入。

《分析野外資料集註釋品質管理》發表的一項研究發現，48% 的受評論文採用標記作為資料註釋的主要形式，而 31% 的論文採用文本準備方法，凸顯了結構化標記在機器學習資料集準備中的優勢。

電腦視覺和自然語言處理的興起

從自動駕駛汽車到臉部辨識，各種各樣的電腦視覺應用都需要精確標記的圖像和視訊資料才能準確運作。同樣，聊天機器人、情緒分析和機器翻譯中使用的自然語言處理 (NLP) 工具也依賴註釋的文本來提升語境理解。隨著人工智慧模型日益複雜，對多樣化、特定領域註釋的需求也日益成長，從而推動了市場擴張。邊緣人工智慧和即時分析的興起進一步提升了可擴展註釋解決方案的重要性。

手動註釋高成本且耗時

手動資料標註仍然是一項勞力密集流程，通常需要經驗豐富的標註人員花費數小時來標記複雜的資料集。這不僅增加了營運成本，還會延遲計劃進度，尤其是在大規模 AI 部署的情況下。醫療保健和自動駕駛等行業對準確性要求極高，因此手動標註必不可少，但效率低。此外，維護跨團隊和跨地區的標註一致性也面臨挑戰，會影響模型準確性。

自動和半自動註釋工具的進步

半自動化平台利用機器學習演算法來提案註釋，然後由專家進行檢驗或修正，從而顯著縮短週轉時間。這些工具正日益與雲端基礎工作流程整合，從而實現遠端協作和即時更新。此外，合成資料產生和傳輸學習的出現，最大限度地減少了大規模手動標註的需求。隨著註釋平台變得更加直覺和可自訂，它們正在為中小企業和學術機構的廣泛採用鋪平道路。

監管不確定性和合規標準的變化

GDPR、HIPAA 等法規結構以及新的 AI 專案法律，對如何收集和處理註釋資料（尤其是個人資料和生物特徵資料）提出了更嚴格的指導方針。企業必須應對不斷變化的合規環境，這些環境因地區和產業而異，增加了跨境營運的複雜性。不遵守這些標準可能會導致法律處罰和聲譽損害。此外，對偏見性註釋和濫用標記資料的倫理擔憂，也要求註釋實踐必須透明且課責。

COVID-19的影響：

疫情加速了各行各業的數位轉型，推動了對人工智慧主導解決方案的需求，並最終推動了標註資料集的需求。隨著遠距辦公成為常態，企業紛紛轉向雲端基礎標註平台，以維持資料標註計劃的連續性。在醫療保健和零售業，需要快速標註新資料類型（從診斷影像到非接觸式購物）的人工智慧應用激增。然而，勞動力供應和供應鏈的初期挑戰阻礙了手動標註工作。

圖像/影片註釋部分預計將在預測期內成為最大的部分

影像/影片註釋領域預計將在預測期內佔據最大的市場佔有率，因為它在實現電腦視覺應用方面發揮關鍵作用。從自主導航系統到監控分析，這些註釋為機器解讀提供了重要的空間和上下文線索。該領域受益於汽車、醫療保健和零售等行業日益成長的需求，這些行業擁有豐富的視覺數據，並且擴大被用於決策。

語意註釋部分預計將在預測期內實現最高的複合年成長率

語義標註領域預計將在預測期內實現最高成長率，這得益於其在提升自然語言處理 (NLP) 能力方面的關鍵作用。透過標記文本中的實體、概念和關係，語義標註使機器能夠更準確地理解上下文、意圖和含義，這在語音助理、法律文件分析和自動化客戶支援等應用中尤其重要。由於知識圖譜和本體論的融合，該領域正在經歷快速成長，這提升了標註的深度和相關性。

佔比最大的地區：

由於新興經濟體人工智慧應用的強勁成長，預計亞太地區將在預測期內佔據最大的市場佔有率。中國、印度和韓國等國家正大力投資人工智慧基礎設施，對註釋資料集的需求龐大。該地區擁有大量熟練的註釋人員和經濟高效的勞動力，使其成為外包註釋服務的中心。此外，政府推動數位轉型和智慧城市發展的舉措正在加速人工智慧解決方案的部署，從而推動了對可擴展註釋平台的需求。

複合年成長率最高的地區：

預計北美將在預測期內實現最高的複合年成長率，這得益於技術創新和企業對人工智慧的大力應用。該地區擁有領先的註釋平台提供者和研究機構，這些提供者和研究機構正在不斷發展註釋方法。自動駕駛汽車、醫療保健人工智慧和國防應用領域的投資不斷增加，催生了對高品質標記數據的需求。此外，嚴格的資料隱私法規正在推動安全合規的註釋工作流程的發展。

提供免費客製化：

此報告的訂閱者將獲得以下免費自訂選項之一：

公司簡介
- 對其他市場公司（最多 3 家公司）進行全面分析
- 主要企業的SWOT分析（最多3家公司）
區域細分
- 根據客戶興趣對主要國家進行的市場估計、預測和複合年成長率（註：基於可行性檢查）
競爭基準化分析
- 根據產品系列、地理分佈和策略聯盟對主要企業基準化分析

北美洲
- 美國
- 加拿大
- 墨西哥
歐洲
- 德國
- 英國
- 義大利
- 法國
- 西班牙
- 其他歐洲國家
亞太地區
- 日本
- 中國
- 印度
- 澳洲
- 紐西蘭
- 韓國
- 其他亞太地區
南美洲
- 阿根廷
- 巴西
- 智利
- 其他南美
中東和非洲
- 沙烏地阿拉伯
- 阿拉伯聯合大公國
- 卡達
- 南非
- 其他中東和非洲地區

第11章重大進展

協議、夥伴關係、合作和合資企業
收購與合併
新產品發布
業務擴展
其他關鍵策略

第12章公司概況

TELUS International
SuperAnnotate
Shaip
Scale AI
Playment
LightTag
Labelbox
Keylabs
iMerit
Hive
Defined.ai
Deepen AI
Cogito Tech
CloudFactory
Appen
Amazon Mechanical Turk
Alegion

簡介目錄圖表

Product Code: SMRC30566

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

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

簡介目錄圖表

List of Tables

Table 1 Global Data Annotation Market Outlook, By Region (2024-2032) ($MN)
Table 2 Global Data Annotation Market Outlook, By Type (2024-2032) ($MN)
Table 3 Global Data Annotation Market Outlook, By Image/Video Annotation (2024-2032) ($MN)
Table 4 Global Data Annotation Market Outlook, By Bounding Box (2024-2032) ($MN)
Table 5 Global Data Annotation Market Outlook, By Polygons & Lines (2024-2032) ($MN)
Table 6 Global Data Annotation Market Outlook, By 3D Annotation/LiDAR (2024-2032) ($MN)
Table 7 Global Data Annotation Market Outlook, By Semantic Segmentation: (2024-2032) ($MN)
Table 8 Global Data Annotation Market Outlook, By Keypoint Annotation (2024-2032) ($MN)
Table 9 Global Data Annotation Market Outlook, By Text Annotation (2024-2032) ($MN)
Table 10 Global Data Annotation Market Outlook, By Named Entity Recognition (NER) (2024-2032) ($MN)
Table 11 Global Data Annotation Market Outlook, By Sentiment Analysis (2024-2032) ($MN)
Table 12 Global Data Annotation Market Outlook, By Document & Content Categorization (2024-2032) ($MN)
Table 13 Global Data Annotation Market Outlook, By Audio Annotation (2024-2032) ($MN)
Table 14 Global Data Annotation Market Outlook, By Speech Transcription (2024-2032) ($MN)
Table 15 Global Data Annotation Market Outlook, By Sound Event Detection (2024-2032) ($MN)
Table 16 Global Data Annotation Market Outlook, By Speaker Identification (2024-2032) ($MN)
Table 17 Global Data Annotation Market Outlook, By Other Types (2024-2032) ($MN)
Table 18 Global Data Annotation Market Outlook, By Method (2024-2032) ($MN)
Table 19 Global Data Annotation Market Outlook, By Manual Annotation (2024-2032) ($MN)
Table 20 Global Data Annotation Market Outlook, By Semi-Supervised/Hybrid Annotation (2024-2032) ($MN)
Table 21 Global Data Annotation Market Outlook, By Automated Annotation (2024-2032) ($MN)
Table 22 Global Data Annotation Market Outlook, By Annotation (2024-2032) ($MN)
Table 23 Global Data Annotation Market Outlook, By Semantic Annotation (2024-2032) ($MN)
Table 24 Global Data Annotation Market Outlook, By Instance Annotation (2024-2032) ($MN)
Table 25 Global Data Annotation Market Outlook, By Polygon Annotation (2024-2032) ($MN)
Table 26 Global Data Annotation Market Outlook, By Bounding Box Annotation (2024-2032) ($MN)
Table 27 Global Data Annotation Market Outlook, By Keypoint Annotation (2024-2032) ($MN)
Table 28 Global Data Annotation Market Outlook, By Other Annotations (2024-2032) ($MN)
Table 29 Global Data Annotation Market Outlook, By Application (2024-2032) ($MN)
Table 30 Global Data Annotation Market Outlook, By Computer Vision (2024-2032) ($MN)
Table 31 Global Data Annotation Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
Table 32 Global Data Annotation Market Outlook, By Healthcare Diagnostics (2024-2032) ($MN)
Table 33 Global Data Annotation Market Outlook, By Robotics (2024-2032) ($MN)
Table 34 Global Data Annotation Market Outlook, By Speech Recognition (2024-2032) ($MN)
Table 35 Global Data Annotation Market Outlook, By E-commerce Personalization (2024-2032) ($MN)
Table 36 Global Data Annotation Market Outlook, By Autonomous Vehicles (2024-2032) ($MN)
Table 37 Global Data Annotation Market Outlook, By Other Applications (2024-2032) ($MN)
Table 38 Global Data Annotation Market Outlook, By End User (2024-2032) ($MN)
Table 39 Global Data Annotation Market Outlook, By IT & Telecom (2024-2032) ($MN)
Table 40 Global Data Annotation Market Outlook, By Government & Defense (2024-2032) ($MN)
Table 41 Global Data Annotation Market Outlook, By BFSI (2024-2032) ($MN)
Table 42 Global Data Annotation Market Outlook, By Retail & E-commerce (2024-2032) ($MN)
Table 43 Global Data Annotation Market Outlook, By Media & Entertainment (2024-2032) ($MN)
Table 44 Global Data Annotation Market Outlook, By Other End Users (2024-2032) ($MN)