Home » Computers & Software, Electronics & Semiconductors, Manufacturing & Industry, Technology, World » Machine Translation Market Will Be Worth USD 983.3 Million by 2022: Research Report by Hexa Reports
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Technological advancements have led to the development of sophisticated translation technology with minimal errors and grammatical coherence, which has considerably widened the scope for machine translation. The ability to rapidly, seamlessly and cost-effectively translate documents and content in regional languages has spurred the “machine translation market” over the past few years.

The global machine translation market is expected to reach USD 983.3 million by 2022. Technological advancements have led to the development of sophisticated translation technology with minimal errors and grammatical coherence, which has considerably widened the scope for machine translation. The ability to rapidly, seamlessly and cost-effectively translate documents and content in regional languages has spurred the machine translation market over the past few years.

The provision of machine translation Software-as-a-Service (MTSaaS) over secure cloud offerings hosted in data centers is expected to offer substantial growth opportunity for the market. Increasing prominence of cloud computing has resulted in growing demand for cloud-based translation tools, which is further expected to boost the machine translation market. Constant R&D and technological advancements have enabled the technology to seamlessly integrate into a range of products from enterprise-level systems to consumer devices. Upgrading of communication networks and the rising proliferation of smartphones has led to the introduction of machine translation apps that provide instant translation of multilingual text.

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Further key findings from the study suggest:

Electronics and automotive industries are expected to gain significant market share by 2022. Application of machine translation in healthcare is expected to gain market share over the next six years. The need for accurate and timely translation of a large amount of content is expected to drive demand in various application industries.

Training of SMT-based translation engines consists of feeding in millions of segments of glossaries, bilingual texts, etc. for the engine to learn by examples. The demand for SMT has significantly increased over the past few years, due to its effectiveness over RBMT in terms of time and cost. It is also expected to remain the largest technology market through 2022.

Government initiatives and the presence of a large number of service providers has helped boost market growth in the U.S. Microsoft and Google, two of the largest technology providers based in the U.S., have made statistical machine translation technology popular with their free online engines.

Machine translation providers often sell products through their own websites or indirectly through a network of specialist sales agents. Most providers prefer having a direct sales force to market and sell their offerings. MT solutions are available as commercial software solutions (e.g., SYSTRAN Enterprise Server and SDL BeGlobal) or as free, web-based applications (e.g., Microsoft Bing Translator and Google Translate).

For the purpose of this study, Grand View Research has segmented the global machine translation market on the basis of application, technology, and region:

Machine Translation Application Outlook (Revenue, USD Million, 2012 – 2022)

Automotive

Military & Defense

Electronics

 IT

Healthcare

Others

Machine Translation Technology Outlook (Revenue, USD Million, 2012 – 2022)

RBMT

SMT

Others

Machine Translation Regional Outlook (Revenue, USD Million, 2012 – 2022)

North America

Europe

Asia Pacific

Latin America

MEA

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Table of Contents:

Chapter 1. Methodology and Scope

Chapter 2. Executive Summary

Chapter 3. Machine Translation Industry Outlook

Chapter 4. Machine Translation Application Outlook

Chapter 5. Machine Translation Technology Outlook

Chapter 6. Machine Translation Regional Outlook

Chapter 7. Competitive Landscape

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