Report Content
Chapter 1 Methodology & Scope
1.1 Market scope & definition
1.2 Base estimates & calculations
1.3 Forecast calculation
1.4 Data sources
1.4.1 Primary
1.4.2 Secondary
1.4.2.1 Paid sources
1.4.2.2 Public sources
Chapter 2 Executive Summary
2.1 Data annotation tools industry 3600 synopsis, 2018 - 2032
2.2 Business trends
2.3 Regional trends
2.4 Data type trends
2.5 Annotation approach trends
2.6 Application trends
Chapter 3 Data Annotation Tools Industry Insights
3.1 Impact of COVID-19 outbreak
3.1.1 North America
3.1.2 Europe
3.1.3 Asia Pacific
3.1.4 Latin America
3.1.5 MEA
3.2 Impacts of the Russia-Ukraine war
3.3 Data annotation tools industry ecosystem analysis
3.3.1 Data annotation software vendors
3.3.2 Cloud service providers
3.3.3 Distributors and resellers
3.3.4 Third party service providers
3.3.5 End-user
3.3.6 Vendor matrix
3.4 Technology & Innovation landscape
3.4.1 Pseudo labelling
3.4.2 Online content moderation
3.5 Patent analysis
3.6 Key news & initiatives
3.7 Regulatory landscape
3.8 Industry impact forces
3.8.1 Growth drivers
3.8.1.1 Rising demand for annotated data to improve machine learning models
3.8.1.2 Increasing investments in the development of autonomous driving technologies
3.8.1.3 Growing adoption of data annotation for medical imaging data
3.8.1.4 Surging uptake of text annotation for document classification
3.8.2 Industry pitfalls & challenges
3.8.2.1 Inaccurate data labelling due to poor content quality
3.8.2.2 Lack of skilled professionals
3.8.2.3 High costs associated with manual data annotation
3.9 Growth potential analysis
3.10 Porter’s analysis
3.11 PESTEL analysis
Chapter 4 Competitive Landscape, 2022
4.1 Introduction
4.2 Company market share, 2022
4.3 Major market players, 2022
4.3.1 AWS
4.3.2 Appen Limited
4.3.3 Google LLC
4.3.4 IBM Corporation
4.3.5 LionBridge AI
4.3.6 Mighty AI
4.3.7 Scale, Inc.
4.4 Competitive positioning matrix
4.5 Strategic outlook matrix
Chapter 5 Data Annotation Tools Market, By Data type
5.1 Key trends, by data type
5.2 Image/video
5.2.1 Market estimates and forecast, 2018 – 2032
5.2.2 Bounding box
5.2.2.1 Market estimates and forecast, 2018 – 2032
5.2.3 Semantic annotation
5.2.3.1 Market estimates and forecast, 2018 – 2032
5.2.4 Polygon annotation
5.2.4.1 Market estimates and forecast, 2018 – 2032
5.2.5 Lines and splines
5.2.5.1 Market estimates and forecast, 2018 – 2032
5.2.6 Others
5.2.6.1 Market estimates and forecast, 2018 – 2032
5.3 Text
5.3.1 Market estimates and forecast, 2018 – 2032
5.4 Audio
5.4.1 Market estimates and forecast, 2018 – 2032
Chapter 6 Data Annotation Tools Market, By Annotation approach
6.1 Key trends, by annotation approach
6.2 Manual annotation
6.2.1 Market estimates and forecast, 2018 – 2032
6.3 Automated annotation
6.3.1 Market estimates and forecast, 2018 – 2032
Chapter 7 Data Annotation Tools Market, By Application
7.1 Key trends, by application
7.2 IT & Telecom
7.2.1 Market estimates and forecast, 2018 – 2032
7.3 BFSI
7.3.1 Market estimates and forecast, 2018 – 2032
7.4 Healthcare
7.4.1 Market estimates and forecast, 2018 – 2032
7.5 Retail
7.5.1 Market estimates and forecast, 2018 – 2032
7.6 Automotive
7.6.1 Market estimates and forecast, 2018 – 2032
7.7 Agriculture
7.7.1 Market estimates and forecast, 2018 – 2032
7.8 Others
7.8.1 Market estimates and forecast, 2018 – 2032
Chapter 8 Data Annotation Tools Market, By Region
8.1 Key trends, by region
8.2 North America
8.2.1 Market estimates and forecast, 2018 – 2032
8.2.2 Market estimates and forecast, by data type, 2018 – 2032
8.2.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.2.4 Market estimates and forecast, by application, 2018 – 2032
8.2.5 U.S.
8.2.5.1 Market estimates and forecast, 2018 – 2032
8.2.5.2 Market estimates and forecast, by data type, 2018 – 2032
8.2.5.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.2.5.4 Market estimates and forecast, by application, 2018 – 2032
8.2.6 Canada
8.2.6.1 Market estimates and forecast, 2018 – 2032
8.2.6.2 Market estimates and forecast, by data type, 2018 – 2032
8.2.6.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.2.6.4 Market estimates and forecast, by application, 2018 – 2032
8.3 Europe
8.3.1 Market estimates and forecast, 2018 – 2032
8.3.2 Market estimates and forecast, by data type, 2018 – 2032
8.3.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.3.4 Market estimates and forecast, by application, 2018 – 2032
8.3.5 UK
8.3.5.1 Market estimates and forecast, 2018 – 2032
8.3.5.2 Market estimates and forecast, by data type, 2018 – 2032
8.3.5.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.3.5.4 Market estimates and forecast, by application, 2018 – 2032
8.3.6 Germany
8.3.6.1 Market estimates and forecast, 2018 – 2032
8.3.6.2 Market estimates and forecast, by data type, 2018 – 2032
8.3.6.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.3.6.4 Market estimates and forecast, by application, 2018 – 2032
8.3.7 France
8.3.7.1 Market estimates and forecast, 2018 – 2032
8.3.7.2 Market estimates and forecast, by data type, 2018 – 2032
8.3.7.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.3.7.4 Market estimates and forecast, by application, 2018 – 2032
8.3.8 Italy
8.3.8.1 Market estimates and forecast, 2018 – 2032
8.3.8.2 Market estimates and forecast, by data type, 2018 – 2032
8.3.8.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.3.8.4 Market estimates and forecast, by application, 2018 – 2032
8.3.9 Spain
8.3.9.1 Market estimates and forecast, 2018 – 2032
8.3.9.2 Market estimates and forecast, by data type, 2018 – 2032
8.3.9.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.3.9.4 Market estimates and forecast, by application, 2018 – 2032
8.3.10 Netherlands
8.3.10.1 Market estimates and forecast, 2018 – 2032
8.3.10.2 Market estimates and forecast, by data type, 2018 – 2032
8.3.10.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.3.10.4 Market estimates and forecast, by application, 2018 – 2032
8.3.11 Nordics
8.3.11.1 Market estimates and forecast, 2018 – 2032
8.3.11.2 Market estimates and forecast, by data type, 2018 – 2032
8.3.11.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.3.11.4 Market estimates and forecast, by application, 2018 – 2032
8.4 Asia Pacific
8.4.1 Market estimates and forecast, 2018 – 2032
8.4.2 Market estimates and forecast, by data type, 2018 – 2032
8.4.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.4.4 Market estimates and forecast, by application, 2018 – 2032
8.4.5 China
8.4.5.1 Market estimates and forecast, 2018 – 2032
8.4.5.2 Market estimates and forecast, by data type, 2018 – 2032
8.4.5.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.4.5.4 Market estimates and forecast, by application, 2018 – 2032
8.4.6 India
8.4.6.1 Market estimates and forecast, 2018 – 2032
8.4.6.2 Market estimates and forecast, by data type, 2018 – 2032
8.4.6.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.4.6.4 Market estimates and forecast, by application, 2018 – 2032
8.4.7 Japan
8.4.7.1 Market estimates and forecast, 2018 – 2032
8.4.7.2 Market estimates and forecast, by data type, 2018 – 2032
8.4.7.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.4.7.4 Market estimates and forecast, by application, 2018 – 2032
8.4.8 South Korea
8.4.8.1 Market estimates and forecast, 2018 – 2032
8.4.8.2 Market estimates and forecast, by data type, 2018 – 2032
8.4.8.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.4.8.4 Market estimates and forecast, by application, 2018 – 2032
8.4.9 Australia
8.4.9.1 Market estimates and forecast, 2018 – 2032
8.4.9.2 Market estimates and forecast, by data type, 2018 – 2032
8.4.9.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.4.9.4 Market estimates and forecast, by application, 2018 – 2032
8.4.10 Singapore
8.4.10.1 Market estimates and forecast, 2018 – 2032
8.4.10.2 Market estimates and forecast, by data type, 2018 – 2032
8.4.10.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.4.10.4 Market estimates and forecast, by application, 2018 – 2032
8.5 Latin America
8.5.1 Market estimates and forecast, 2018 – 2032
8.5.2 Market estimates and forecast, by data type, 2018 – 2032
8.5.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.5.4 Market estimates and forecast, by application, 2018 – 2032
8.5.5 Brazil
8.5.5.1 Market estimates and forecast, 2018 – 2032
8.5.5.2 Market estimates and forecast, by data type, 2018 – 2032
8.5.5.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.5.5.4 Market estimates and forecast, by application, 2018 – 2032
8.5.6 Mexico
8.5.6.1 Market estimates and forecast, 2018 – 2032
8.5.6.2 Market estimates and forecast, by data type, 2018 – 2032
8.5.6.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.5.6.4 Market estimates and forecast, by application, 2018 – 2032
8.5.7 Colombia
8.5.7.1 Market estimates and forecast, 2018 – 2032
8.5.7.2 Market estimates and forecast, by data type, 2018 – 2032
8.5.7.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.5.7.4 Market estimates and forecast, by application, 2018 – 2032
8.6 MEA
8.6.1 Market estimates and forecast, 2018 – 2032
8.6.2 Market estimates and forecast, by data type, 2018 – 2032
8.6.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.6.4 Market estimates and forecast, by application, 2018 – 2032
8.6.5 UAE
8.6.5.1 Market estimates and forecast, 2018 – 2032
8.6.5.2 Market estimates and forecast, by data type, 2018 – 2032
8.6.5.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.6.5.4 Market estimates and forecast, by application, 2018 – 2032
8.6.6 South Africa
8.6.6.1 Market estimates and forecast, 2018 – 2032
8.6.6.2 Market estimates and forecast, by data type, 2018 – 2032
8.6.6.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.6.6.4 Market estimates and forecast, by application, 2018 – 2032
8.6.7 Saudi Arabia
8.6.7.1 Market estimates and forecast, 2018 – 2032
8.6.7.2 Market estimates and forecast, by data type, 2018 – 2032
8.6.7.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.6.7.4 Market estimates and forecast, by application, 2018 – 2032
8.6.8 Israel
8.6.8.1 Market estimates and forecast, 2018 – 2032
8.6.8.2 Market estimates and forecast, by data type, 2018 – 2032
8.6.8.3 Market estimates and forecast, by annotation approach, 2018 – 2032
8.6.8.4 Market estimates and forecast, by application, 2018 – 2032
Chapter 9 Company Profiles
9.1 Alegion Inc.
9.1.1 Business Overview
9.1.2 Financial Data
9.1.3 Data type Landscape
9.1.4 Strategic Outlook
9.1.5 SWOT Analysis
9.2 Appen Limited
9.2.1 Business Overview
9.2.2 Financial Data
9.2.3 Data type Landscape
9.2.4 Strategic Outlook
9.2.5 SWOT Analysis
9.3 Amazon Web Services, Inc. (Amazon.com, Inc)
9.3.1 Business Overview
9.3.2 Financial Data
9.3.3 Data type Landscape
9.3.4 Strategic Outlook
9.3.5 SWOT Analysis
9.4 Clickworker GmBH
9.4.1 Business Overview
9.4.2 Financial Data
9.4.3 Data type Landscape
9.4.4 Strategic Outlook
9.4.5 SWOT Analysis
9.5 CloudApp, Inc.
9.5.1 Business Overview
9.5.2 Financial Data
9.5.3 Data type Landscape
9.5.4 Strategic Outlook
9.5.5 SWOT Analysis
9.6 CloudFactory Limited
9.6.1 Business Overview
9.6.2 Financial Data
9.6.3 Data type Landscape
9.6.4 Strategic Outlook
9.6.5 SWOT Analysis
9.7 Cogito Tech LLC
9.7.1 Business Overview
9.7.2 Financial Data
9.7.3 Data type Landscape
9.7.4 Strategic Outlook
9.7.5 SWOT Analysis
9.8 Dataturks (Walmart Labs)
9.8.1 Business Overview
9.8.2 Financial Data
9.8.3 Data type Landscape
9.8.4 Strategic Outlook
9.8.5 SWOT Analysis
9.9 Defined AI
9.9.1 Business Overview
9.9.2 Financial Data
9.9.3 Data type Landscape
9.9.4 Strategic Outlook
9.9.5 SWOT Analysis
9.10 Google LLC. (Alphabet Inc.)
9.10.1 Business Overview
9.10.2 Financial Data
9.10.3 Data type Landscape
9.10.4 Strategic Outlook
9.10.5 SWOT Analysis
9.11 Hive (Castle Global, Inc)
9.11.1 Business Overview
9.11.2 Financial Data
9.11.3 Data type Landscape
9.11.4 Strategic Outlook
9.11.5 SWOT Analysis
9.12 IBM Corporation
9.12.1 Business Overview
9.12.2 Financial Data
9.12.3 Data type Landscape
9.12.4 Strategic Outlook
9.12.5 SWOT Analysis
9.13 iMerit
9.13.1 Business Overview
9.13.2 Financial Data
9.13.3 Data type Landscape
9.13.4 Strategic Outlook
9.13.5 SWOT Analysis
9.14 Labelbox, Inc.
9.14.1 Business Overview
9.14.2 Financial Data
9.14.3 Data type Landscape
9.14.4 Strategic Outlook
9.14.5 SWOT Analysis
9.15 Landing AI
9.15.1 Business Overview
9.15.2 Financial Data
9.15.3 Data type Landscape
9.15.4 Strategic Outlook
9.15.5 SWOT Analysis
9.16 Lionbridge AI
9.16.1 Business Overview
9.16.2 Financial Data
9.16.3 Data type Landscape
9.16.4 Strategic Outlook
9.16.5 SWOT Analysis
9.17 Mighty AI
9.17.1 Business Overview
9.17.2 Financial Data
9.17.3 Data type Landscape
9.17.4 Strategic Outlook
9.17.5 SWOT Analysis
9.18 MonkeyLearn Inc.
9.18.1 Business Overview
9.18.2 Financial Data
9.18.3 Data type Landscape
9.18.4 Strategic Outlook
9.18.5 SWOT Analysis
9.19 Neurala Inc.
9.19.1 Business Overview
9.19.2 Financial Data
9.19.3 Data type Landscape
9.19.4 Strategic Outlook
9.20 Playment Inc.
9.20.1 Business Overview
9.20.2 Financial Data
9.20.3 Data type Landscape
9.20.4 Strategic Outlook
9.20.5 SWOT Analysis
9.21 Samasource Inc.
9.21.1 Business Overview
9.21.2 Financial Data
9.21.3 Data type Landscape
9.21.4 Strategic Outlook
9.21.5 SWOT Analysis
9.22 Scale AI, Inc.
9.22.1 Business Overview
9.22.2 Financial Data
9.22.3 Data type Landscape
9.22.4 Strategic Outlook
9.22.5 SWOT Analysis
9.23 Sigma AI
9.23.1 Business Overview
9.23.2 Financial Data
9.23.3 Data type Landscape
9.23.4 Strategic Outlook
9.24 Webtunix AI
9.24.1 Business Overview
9.24.2 Financial Data
9.24.3 Data type Landscape
9.24.4 Strategic Outlook
9.24.5 SWOT Analysis
