Global Machine Translation

Machine Translation Comprehensive Study by Type (Rule-Based Machine Translation (RBMT), Statistical Machine Translation (SMT), Example-Based Machine Translation (EBMT), Hybrid Machine Translation (HMT), Neural Machine Translation (MT)), Industry Verticals (IT & Telecommunication, Military & Defense, Legal, E-commerce, Healthcare, Others), Offering (Software, Services {Multi-Domain Machine Translation Services, Specialized Machine Translation Services}) Players and Region - Global Market Outlook to 2026

Machine Translation Market by XX Submarkets | Forecast Years 2022-2027 | CAGR: 14.12%  

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  • Summary
  • Market Segments
  • Table of Content
  • List of Table & Figures
  • Players Profiled
Machine Translation Market Scope
Over the past few decades, due to increasing international trades across the globe, the need for language translators is needed. In addition to this, manual translators are time-consuming and not totally reliable. Thus, the introduction to machine translators has minimized the time consumed as well as increased the reliability of the machine translators. Machine translation (MT) is an automated translation process by which computer software is used to translate a text from one natural language (such as mostly used language like English) to another language (Italian, Spanish and many others). The increasing demand from numerous applications will further show lucrative growth over the forecasted period.

According to AMA, the Global Machine Translation market is expected to see growth rate of 14.12% and may see market size of USD1290.98 Million by 2026.

Research Analyst at AMA estimates that European Players will contribute to the maximum growth of Global Machine Translation market throughout the predicted period.

Applications Technology (AppTek) (United States), Asia Online Pte Ltd. (Singapore), Cloudwords Inc. (United States), IBM Corporation (United States), Lighthouse IP Group (United States), Lingo24 Ltd. (United Kingdom), Lingotek Inc. (United States), Lionbridge Technologies, Inc. (United States), Lucy Software and Services GmbH (Germany), RWS Holdings (Moravia IT) (United Kingdom), Pangeanic (Spain) and ProMT (Russia) are some of the key players that are part of study coverage. Additionally, the Players which are also part of the research are BBN Technologies (United States), SDL plc (United Kingdom), Smart Communications, Inc. (Philippines), Systran International (United States) and Welocalize Inc.(United States).

About Approach
The research aims to propose a patent-based approach in searching for potential technology partners as a supporting tool for enabling open innovation. The study also proposes a systematic searching process of technology partners as a
preliminary step to select the emerging and key players that are involved in implementing market estimations. While patent analysis is employed to overcome the aforementioned data- and process-related limitations, as expenses occurred in that technology allows us to estimate the market size by evolving segments as target market from total available market.

Segmentation Overview
The study have segmented the market of Global Machine Translation market by Type (Rule-Based Machine Translation (RBMT), Statistical Machine Translation (SMT), Example-Based Machine Translation (EBMT), Hybrid Machine Translation (HMT) and Neural Machine Translation (MT)) and Region with country level break-up.

On the basis of geography, the market of Machine Translation has been segmented into South America (Brazil, Argentina, Rest of South America), Asia Pacific (China, Japan, India, South Korea, Taiwan, Australia, Rest of Asia-Pacific), Europe (Germany, France, Italy, United Kingdom, Netherlands, Rest of Europe), MEA (Middle East, Africa), North America (United States, Canada, Mexico).




Market Trend
  • Increasing Adoption of Artificially Intelligent Machine Translators
  • Introduction to Video Localization Leading to Increasing Regional Expansion

Market Drivers
  • Upsurging Need for Timely and Accurate Translation of Large Amounts
  • Existence of Larger Developers and Increasing Government Initiatives Assisting Machine Translation

Opportunities
  • Time Saving Operations has led to Improved Business Growth
  • Provides Comparatively Cheaper Solution for Software Solutions

Restraints
  • Complexities in Maintaining Operational Consistency and Reliability
  • Lack of Skilled Workforce Required for Developing Machine Translators

Challenges
  • Hard to Design Automated Translator Based on Human Intelligence and Error Handling Capability


Key Target Audience
Machine Translation Providers, Machine Translator Manufacturers, Machine Translator Distributors and Suppliers, Machine Translators Industry Associations, Research and Development Institutes, Government Agencies, Upstream and Downstream Buyers and Others

Report Objectives / Segmentation Covered

By Type
  • Rule-Based Machine Translation (RBMT)
  • Statistical Machine Translation (SMT)
  • Example-Based Machine Translation (EBMT)
  • Hybrid Machine Translation (HMT)
  • Neural Machine Translation (MT)
By Industry Verticals
  • IT & Telecommunication
  • Military & Defense
  • Legal
  • E-commerce
  • Healthcare
  • Others

By Offering
  • Software
  • Services {Multi-Domain Machine Translation Services, Specialized Machine Translation Services}

By Regions
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Taiwan
    • Australia
    • Rest of Asia-Pacific
  • Europe
    • Germany
    • France
    • Italy
    • United Kingdom
    • Netherlands
    • Rest of Europe
  • MEA
    • Middle East
    • Africa
  • North America
    • United States
    • Canada
    • Mexico
  • 1. Market Overview
    • 1.1. Introduction
    • 1.2. Scope/Objective of the Study
      • 1.2.1. Research Objective
  • 2. Executive Summary
    • 2.1. Introduction
  • 3. Market Dynamics
    • 3.1. Introduction
    • 3.2. Market Drivers
      • 3.2.1. Upsurging Need for Timely and Accurate Translation of Large Amounts
      • 3.2.2. Existence of Larger Developers and Increasing Government Initiatives Assisting Machine Translation
    • 3.3. Market Challenges
      • 3.3.1. Hard to Design Automated Translator Based on Human Intelligence and Error Handling Capability
    • 3.4. Market Trends
      • 3.4.1. Increasing Adoption of Artificially Intelligent Machine Translators
      • 3.4.2. Introduction to Video Localization Leading to Increasing Regional Expansion
  • 4. Market Factor Analysis
    • 4.1. Porters Five Forces
    • 4.2. Supply/Value Chain
    • 4.3. PESTEL analysis
    • 4.4. Market Entropy
    • 4.5. Patent/Trademark Analysis
  • 5. Global Machine Translation, by Type, Industry Verticals, Offering and Region (value) (2015-2020)
    • 5.1. Introduction
    • 5.2. Global Machine Translation (Value)
      • 5.2.1. Global Machine Translation by: Type (Value)
        • 5.2.1.1. Rule-Based Machine Translation (RBMT)
        • 5.2.1.2. Statistical Machine Translation (SMT)
        • 5.2.1.3. Example-Based Machine Translation (EBMT)
        • 5.2.1.4. Hybrid Machine Translation (HMT)
        • 5.2.1.5. Neural Machine Translation (MT)
      • 5.2.2. Global Machine Translation by: Industry Verticals (Value)
        • 5.2.2.1. IT & Telecommunication
        • 5.2.2.2. Military & Defense
        • 5.2.2.3. Legal
        • 5.2.2.4. E-commerce
        • 5.2.2.5. Healthcare
        • 5.2.2.6. Others
      • 5.2.3. Global Machine Translation by: Offering (Value)
        • 5.2.3.1. Software
        • 5.2.3.2. Services {Multi-Domain Machine Translation Services, Specialized Machine Translation Services}
      • 5.2.4. Global Machine Translation Region
        • 5.2.4.1. South America
          • 5.2.4.1.1. Brazil
          • 5.2.4.1.2. Argentina
          • 5.2.4.1.3. Rest of South America
        • 5.2.4.2. Asia Pacific
          • 5.2.4.2.1. China
          • 5.2.4.2.2. Japan
          • 5.2.4.2.3. India
          • 5.2.4.2.4. South Korea
          • 5.2.4.2.5. Taiwan
          • 5.2.4.2.6. Australia
          • 5.2.4.2.7. Rest of Asia-Pacific
        • 5.2.4.3. Europe
          • 5.2.4.3.1. Germany
          • 5.2.4.3.2. France
          • 5.2.4.3.3. Italy
          • 5.2.4.3.4. United Kingdom
          • 5.2.4.3.5. Netherlands
          • 5.2.4.3.6. Rest of Europe
        • 5.2.4.4. MEA
          • 5.2.4.4.1. Middle East
          • 5.2.4.4.2. Africa
        • 5.2.4.5. North America
          • 5.2.4.5.1. United States
          • 5.2.4.5.2. Canada
          • 5.2.4.5.3. Mexico
  • 6. Machine Translation: Manufacturers/Players Analysis
    • 6.1. Competitive Landscape
      • 6.1.1. Market Share Analysis
        • 6.1.1.1. Top 3
        • 6.1.1.2. Top 5
    • 6.2. Peer Group Analysis (2020)
    • 6.3. BCG Matrix
    • 6.4. Company Profile
      • 6.4.1. Applications Technology (AppTek) (United States)
        • 6.4.1.1. Business Overview
        • 6.4.1.2. Products/Services Offerings
        • 6.4.1.3. Financial Analysis
        • 6.4.1.4. SWOT Analysis
      • 6.4.2. Asia Online Pte Ltd. (Singapore)
        • 6.4.2.1. Business Overview
        • 6.4.2.2. Products/Services Offerings
        • 6.4.2.3. Financial Analysis
        • 6.4.2.4. SWOT Analysis
      • 6.4.3. Cloudwords Inc. (United States)
        • 6.4.3.1. Business Overview
        • 6.4.3.2. Products/Services Offerings
        • 6.4.3.3. Financial Analysis
        • 6.4.3.4. SWOT Analysis
      • 6.4.4. IBM Corporation (United States)
        • 6.4.4.1. Business Overview
        • 6.4.4.2. Products/Services Offerings
        • 6.4.4.3. Financial Analysis
        • 6.4.4.4. SWOT Analysis
      • 6.4.5. Lighthouse IP Group (United States)
        • 6.4.5.1. Business Overview
        • 6.4.5.2. Products/Services Offerings
        • 6.4.5.3. Financial Analysis
        • 6.4.5.4. SWOT Analysis
      • 6.4.6. Lingo24 Ltd. (United Kingdom)
        • 6.4.6.1. Business Overview
        • 6.4.6.2. Products/Services Offerings
        • 6.4.6.3. Financial Analysis
        • 6.4.6.4. SWOT Analysis
      • 6.4.7. Lingotek Inc. (United States)
        • 6.4.7.1. Business Overview
        • 6.4.7.2. Products/Services Offerings
        • 6.4.7.3. Financial Analysis
        • 6.4.7.4. SWOT Analysis
      • 6.4.8. Lionbridge Technologies, Inc. (United States)
        • 6.4.8.1. Business Overview
        • 6.4.8.2. Products/Services Offerings
        • 6.4.8.3. Financial Analysis
        • 6.4.8.4. SWOT Analysis
      • 6.4.9. Lucy Software and Services GmbH (Germany)
        • 6.4.9.1. Business Overview
        • 6.4.9.2. Products/Services Offerings
        • 6.4.9.3. Financial Analysis
        • 6.4.9.4. SWOT Analysis
      • 6.4.10. RWS Holdings (Moravia IT) (United Kingdom)
        • 6.4.10.1. Business Overview
        • 6.4.10.2. Products/Services Offerings
        • 6.4.10.3. Financial Analysis
        • 6.4.10.4. SWOT Analysis
      • 6.4.11. Pangeanic (Spain)
        • 6.4.11.1. Business Overview
        • 6.4.11.2. Products/Services Offerings
        • 6.4.11.3. Financial Analysis
        • 6.4.11.4. SWOT Analysis
      • 6.4.12. ProMT (Russia)
        • 6.4.12.1. Business Overview
        • 6.4.12.2. Products/Services Offerings
        • 6.4.12.3. Financial Analysis
        • 6.4.12.4. SWOT Analysis
  • 7. Appendix
    • 7.1. Acronyms
  • 8. Methodology and Data Source
    • 8.1. Methodology/Research Approach
      • 8.1.1. Research Programs/Design
      • 8.1.2. Market Size Estimation
      • 8.1.3. Market Breakdown and Data Triangulation
    • 8.2. Data Source
      • 8.2.1. Secondary Sources
      • 8.2.2. Primary Sources
    • 8.3. Disclaimer
List of Tables
  • Table 1. Machine Translation: by Type(USD Million)
  • Table 2. Machine Translation Rule-Based Machine Translation (RBMT) , by Region USD Million (2015-2020)
  • Table 3. Machine Translation Statistical Machine Translation (SMT) , by Region USD Million (2015-2020)
  • Table 4. Machine Translation Example-Based Machine Translation (EBMT) , by Region USD Million (2015-2020)
  • Table 5. Machine Translation Hybrid Machine Translation (HMT) , by Region USD Million (2015-2020)
  • Table 6. Machine Translation Neural Machine Translation (MT) , by Region USD Million (2015-2020)
  • Table 7. Machine Translation: by Industry Verticals(USD Million)
  • Table 8. Machine Translation IT & Telecommunication , by Region USD Million (2015-2020)
  • Table 9. Machine Translation Military & Defense , by Region USD Million (2015-2020)
  • Table 10. Machine Translation Legal , by Region USD Million (2015-2020)
  • Table 11. Machine Translation E-commerce , by Region USD Million (2015-2020)
  • Table 12. Machine Translation Healthcare , by Region USD Million (2015-2020)
  • Table 13. Machine Translation Others , by Region USD Million (2015-2020)
  • Table 14. Machine Translation: by Offering(USD Million)
  • Table 15. Machine Translation Software , by Region USD Million (2015-2020)
  • Table 16. Machine Translation Services {Multi-Domain Machine Translation Services, Specialized Machine Translation Services} , by Region USD Million (2015-2020)
  • Table 17. South America Machine Translation, by Country USD Million (2015-2020)
  • Table 18. South America Machine Translation, by Type USD Million (2015-2020)
  • Table 19. South America Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 20. South America Machine Translation, by Offering USD Million (2015-2020)
  • Table 21. Brazil Machine Translation, by Type USD Million (2015-2020)
  • Table 22. Brazil Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 23. Brazil Machine Translation, by Offering USD Million (2015-2020)
  • Table 24. Argentina Machine Translation, by Type USD Million (2015-2020)
  • Table 25. Argentina Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 26. Argentina Machine Translation, by Offering USD Million (2015-2020)
  • Table 27. Rest of South America Machine Translation, by Type USD Million (2015-2020)
  • Table 28. Rest of South America Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 29. Rest of South America Machine Translation, by Offering USD Million (2015-2020)
  • Table 30. Asia Pacific Machine Translation, by Country USD Million (2015-2020)
  • Table 31. Asia Pacific Machine Translation, by Type USD Million (2015-2020)
  • Table 32. Asia Pacific Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 33. Asia Pacific Machine Translation, by Offering USD Million (2015-2020)
  • Table 34. China Machine Translation, by Type USD Million (2015-2020)
  • Table 35. China Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 36. China Machine Translation, by Offering USD Million (2015-2020)
  • Table 37. Japan Machine Translation, by Type USD Million (2015-2020)
  • Table 38. Japan Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 39. Japan Machine Translation, by Offering USD Million (2015-2020)
  • Table 40. India Machine Translation, by Type USD Million (2015-2020)
  • Table 41. India Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 42. India Machine Translation, by Offering USD Million (2015-2020)
  • Table 43. South Korea Machine Translation, by Type USD Million (2015-2020)
  • Table 44. South Korea Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 45. South Korea Machine Translation, by Offering USD Million (2015-2020)
  • Table 46. Taiwan Machine Translation, by Type USD Million (2015-2020)
  • Table 47. Taiwan Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 48. Taiwan Machine Translation, by Offering USD Million (2015-2020)
  • Table 49. Australia Machine Translation, by Type USD Million (2015-2020)
  • Table 50. Australia Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 51. Australia Machine Translation, by Offering USD Million (2015-2020)
  • Table 52. Rest of Asia-Pacific Machine Translation, by Type USD Million (2015-2020)
  • Table 53. Rest of Asia-Pacific Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 54. Rest of Asia-Pacific Machine Translation, by Offering USD Million (2015-2020)
  • Table 55. Europe Machine Translation, by Country USD Million (2015-2020)
  • Table 56. Europe Machine Translation, by Type USD Million (2015-2020)
  • Table 57. Europe Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 58. Europe Machine Translation, by Offering USD Million (2015-2020)
  • Table 59. Germany Machine Translation, by Type USD Million (2015-2020)
  • Table 60. Germany Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 61. Germany Machine Translation, by Offering USD Million (2015-2020)
  • Table 62. France Machine Translation, by Type USD Million (2015-2020)
  • Table 63. France Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 64. France Machine Translation, by Offering USD Million (2015-2020)
  • Table 65. Italy Machine Translation, by Type USD Million (2015-2020)
  • Table 66. Italy Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 67. Italy Machine Translation, by Offering USD Million (2015-2020)
  • Table 68. United Kingdom Machine Translation, by Type USD Million (2015-2020)
  • Table 69. United Kingdom Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 70. United Kingdom Machine Translation, by Offering USD Million (2015-2020)
  • Table 71. Netherlands Machine Translation, by Type USD Million (2015-2020)
  • Table 72. Netherlands Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 73. Netherlands Machine Translation, by Offering USD Million (2015-2020)
  • Table 74. Rest of Europe Machine Translation, by Type USD Million (2015-2020)
  • Table 75. Rest of Europe Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 76. Rest of Europe Machine Translation, by Offering USD Million (2015-2020)
  • Table 77. MEA Machine Translation, by Country USD Million (2015-2020)
  • Table 78. MEA Machine Translation, by Type USD Million (2015-2020)
  • Table 79. MEA Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 80. MEA Machine Translation, by Offering USD Million (2015-2020)
  • Table 81. Middle East Machine Translation, by Type USD Million (2015-2020)
  • Table 82. Middle East Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 83. Middle East Machine Translation, by Offering USD Million (2015-2020)
  • Table 84. Africa Machine Translation, by Type USD Million (2015-2020)
  • Table 85. Africa Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 86. Africa Machine Translation, by Offering USD Million (2015-2020)
  • Table 87. North America Machine Translation, by Country USD Million (2015-2020)
  • Table 88. North America Machine Translation, by Type USD Million (2015-2020)
  • Table 89. North America Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 90. North America Machine Translation, by Offering USD Million (2015-2020)
  • Table 91. United States Machine Translation, by Type USD Million (2015-2020)
  • Table 92. United States Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 93. United States Machine Translation, by Offering USD Million (2015-2020)
  • Table 94. Canada Machine Translation, by Type USD Million (2015-2020)
  • Table 95. Canada Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 96. Canada Machine Translation, by Offering USD Million (2015-2020)
  • Table 97. Mexico Machine Translation, by Type USD Million (2015-2020)
  • Table 98. Mexico Machine Translation, by Industry Verticals USD Million (2015-2020)
  • Table 99. Mexico Machine Translation, by Offering USD Million (2015-2020)
  • Table 100. Company Basic Information, Sales Area and Its Competitors
  • Table 101. Company Basic Information, Sales Area and Its Competitors
  • Table 102. Company Basic Information, Sales Area and Its Competitors
  • Table 103. Company Basic Information, Sales Area and Its Competitors
  • Table 104. Company Basic Information, Sales Area and Its Competitors
  • Table 105. Company Basic Information, Sales Area and Its Competitors
  • Table 106. Company Basic Information, Sales Area and Its Competitors
  • Table 107. Company Basic Information, Sales Area and Its Competitors
  • Table 108. Company Basic Information, Sales Area and Its Competitors
  • Table 109. Company Basic Information, Sales Area and Its Competitors
  • Table 110. Company Basic Information, Sales Area and Its Competitors
  • Table 111. Company Basic Information, Sales Area and Its Competitors
  • Table 112. Research Programs/Design for This Report
  • Table 113. Key Data Information from Secondary Sources
  • Table 114. Key Data Information from Primary Sources
List of Figures
  • Figure 1. Porters Five Forces
  • Figure 2. Supply/Value Chain
  • Figure 3. PESTEL analysis
  • Figure 4. Global Machine Translation: by Type USD Million (2015-2020)
  • Figure 5. Global Machine Translation: by Industry Verticals USD Million (2015-2020)
  • Figure 6. Global Machine Translation: by Offering USD Million (2015-2020)
  • Figure 7. South America Machine Translation Share (%), by Country
  • Figure 8. Asia Pacific Machine Translation Share (%), by Country
  • Figure 9. Europe Machine Translation Share (%), by Country
  • Figure 10. MEA Machine Translation Share (%), by Country
  • Figure 11. North America Machine Translation Share (%), by Country
  • Figure 12. Global Machine Translation share by Players 2020 (%)
  • Figure 13. Global Machine Translation share by Players (Top 3) 2020(%)
  • Figure 14. Global Machine Translation share by Players (Top 5) 2020(%)
  • Figure 15. BCG Matrix for key Companies
  • Figure 16. Applications Technology (AppTek) (United States) Revenue, Net Income and Gross profit
  • Figure 17. Applications Technology (AppTek) (United States) Revenue: by Geography 2020
  • Figure 18. Asia Online Pte Ltd. (Singapore) Revenue, Net Income and Gross profit
  • Figure 19. Asia Online Pte Ltd. (Singapore) Revenue: by Geography 2020
  • Figure 20. Cloudwords Inc. (United States) Revenue, Net Income and Gross profit
  • Figure 21. Cloudwords Inc. (United States) Revenue: by Geography 2020
  • Figure 22. IBM Corporation (United States) Revenue, Net Income and Gross profit
  • Figure 23. IBM Corporation (United States) Revenue: by Geography 2020
  • Figure 24. Lighthouse IP Group (United States) Revenue, Net Income and Gross profit
  • Figure 25. Lighthouse IP Group (United States) Revenue: by Geography 2020
  • Figure 26. Lingo24 Ltd. (United Kingdom) Revenue, Net Income and Gross profit
  • Figure 27. Lingo24 Ltd. (United Kingdom) Revenue: by Geography 2020
  • Figure 28. Lingotek Inc. (United States) Revenue, Net Income and Gross profit
  • Figure 29. Lingotek Inc. (United States) Revenue: by Geography 2020
  • Figure 30. Lionbridge Technologies, Inc. (United States) Revenue, Net Income and Gross profit
  • Figure 31. Lionbridge Technologies, Inc. (United States) Revenue: by Geography 2020
  • Figure 32. Lucy Software and Services GmbH (Germany) Revenue, Net Income and Gross profit
  • Figure 33. Lucy Software and Services GmbH (Germany) Revenue: by Geography 2020
  • Figure 34. RWS Holdings (Moravia IT) (United Kingdom) Revenue, Net Income and Gross profit
  • Figure 35. RWS Holdings (Moravia IT) (United Kingdom) Revenue: by Geography 2020
  • Figure 36. Pangeanic (Spain) Revenue, Net Income and Gross profit
  • Figure 37. Pangeanic (Spain) Revenue: by Geography 2020
  • Figure 38. ProMT (Russia) Revenue, Net Income and Gross profit
  • Figure 39. ProMT (Russia) Revenue: by Geography 2020
List of companies from research coverage that are profiled in the study
  • Applications Technology (AppTek) (United States)
  • Asia Online Pte Ltd. (Singapore)
  • Cloudwords Inc. (United States)
  • IBM Corporation (United States)
  • Lighthouse IP Group (United States)
  • Lingo24 Ltd. (United Kingdom)
  • Lingotek Inc. (United States)
  • Lionbridge Technologies, Inc. (United States)
  • Lucy Software and Services GmbH (Germany)
  • RWS Holdings (Moravia IT) (United Kingdom)
  • Pangeanic (Spain)
  • ProMT (Russia)
Additional players considered in the study are as follows:
BBN Technologies (United States) , SDL plc (United Kingdom) , Smart Communications, Inc. (Philippines) , Systran International (United States) , Welocalize Inc.(United States)
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Feb 2022 203 Pages 92 Tables Base Year: 2021 Coverage: 15+ Companies; 18 Countries

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Frequently Asked Questions (FAQ):

The Machine Translation study can be customized to meet your requirements. The market size breakdown by type [Rule-Based Machine Translation (RBMT), Statistical Machine Translation (SMT), Example-Based Machine Translation (EBMT), Hybrid Machine Translation (HMT) and Neural Machine Translation (MT)], by end use application [].
The Machine Translation Market is gaining popularity and expected to see strong valuation by 2026.
According to AMA, the Global Machine Translation market is expected to see growth rate of 14.12%.

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