The global machine translation market was worth USD 0.61 billion in 2023. The global market is predicted to reach USD 0.68 billion in 2024 and USD 1.59 billion by 2032, growing at a CAGR of 11.23% during the forecast period.
A growing number of internet users worldwide has widened the demand for cross-cultural interaction and understanding of content over the web, which is one of the major driving factors of the global Machine Translation market. Another major driving factor was technological advancements that resulted in the development of translation theory with minimal errors and grammatical consistency, which is likely to propel the growth of revenue worldwide in the machine translation market.
Advancements in machine translation technology and new implementation approaches, including crowdsourcing, are some of the growth-supporting factors in the machine translation market based on revenue rate. Another significant aspect driving growth in the Machine Translation market is the increasing need for content localization and the growing need for cost-effective and high-speed translation.
The high cost of manufacturing equipment is a principal factor limiting the growth of the global Machine Translation Market. Another major restraint affecting the growth of the market is the easy availability of alternative products.
REPORT METRIC |
DETAILS |
Market Size Available |
2023 to 2032 |
Base Year |
2023 |
Forecast Period |
2024 to 2032 |
CAGR |
11.23% |
Segments Covered |
By Technology, Deployment Model, End User, and Region. |
Various Analyses Covered |
Global, Regional & Country Level Analysis, Segment-Level Analysis, DROC, PESTLE Analysis, Porter’s Five Forces Analysis, Competitive Landscape, Analyst Overview on Investment Opportunities |
Regions Covered |
North America, Europe, APAC, Latin America, Middle East & Africa |
Market Leaders Profiled |
Microsoft Corporation, Google, Inc., SDL PLC, Alibaba Cloud, AWS, Baidu, IBM Corporation, Raytheon BBN Technologies Corp, Welocalize Inc, Tencent Cloud TMT, Lionbridge, and Others. |
The Machine Translation market is divided into statistical machine translation, rule-based machine translation, neural machine translation, and other technologies. The neural translation segment holds the major share of the market as it is continuously improved and refined and is anticipated to become even better in the next few years.
The market for machine translation is bifurcated into two types: on-premises and cloud. Among these, the cloud segment holds the dominant share of the market.
The Machine Translation market is segmented into automotive, military and defense, healthcare, IT, e-commerce, and other end-user industries. The IT sector accounted for a prominent share of the market.
The North American region holds the major market share owing to the growing demands across the United States IT sector and the rapid increase of prominent players in the region. This is also estimated to help the machine translation market in North America grow in the coming days.
The Asia Pacific region holds the second largest share of the Machine Translation market because of the increasing number of internet users across the region, especially in countries such as China and India. This is crucial in determining the development rate of the Machine Translation market in this locale.
Europe region is expected to record a surge in its market value of Machine Translations owing to the rising investment for new product innovation by the companies that are supposed to boost the market demand in the foreseen years,
Some of the key players in the global machine translation market include Microsoft Corporation, Google, Inc., SDL PLC, Alibaba Cloud, AWS, Baidu, IBM Corporation, Raytheon BBN Technologies Corp, Welocalize Inc., Tencent Cloud TMT, and Lionbridge.
In the year 2016, Google Inc. launched the NMT tool that deploys an artificial neural network to undertake the multilingual translations of decoding semantic data.
In October 2017, Google Inc. launched Pixel Buds, which consists of a built-in Google Assistant and Google Translate, which can translate 40 different languages using Google Translate technology.
In December 2014, Microsoft Corporation released a preview version of Skype Translator for Spanish and English audiences. This program was developed to combine features of machine learning, speech recognition, and Machine translation.
By Technology
Statistical Machine Translation
Rule-Based Machine Translation
Neural Machine Translation
By Deployment Model
Cloud
On-Premises
By End User
Automotive
Military and Defense
Healthcare
IT
E-Commerce
By Region
North America
The United States
Canada
Rest of North America
Europe
The United Kingdom
Spain
Germany
Italy
France
Rest of Europe
The Asia Pacific
India
Japan
China
Australia
Singapore
Malaysia
South Korea
New Zealand
Southeast Asia
Latin America
Brazil
Argentina
Mexico
Rest of LATAM
The Middle East and Africa
Saudi Arabia
UAE
Lebanon
Jordan
Cyprus
Frequently Asked Questions
Factors such as increasing globalization of businesses, rising demand for localization of content, advancements in artificial intelligence and natural language processing technologies, and the need for cost-effective and efficient translation solutions are driving the growth of the machine translation market.
Challenges include maintaining translation quality and accuracy, especially for complex or nuanced content, overcoming language nuances and cultural differences, ensuring data privacy and security, and addressing concerns regarding the impact on human translators' job roles.
Neural machine translation, a type of machine translation that uses artificial neural networks to predict translations, has significantly improved translation quality and fluency compared to traditional statistical machine translation methods. Its adoption is driving the growth of the machine translation market by offering more accurate and contextually aware translations.
Future trends in the machine translation market include the integration of machine translation with other technologies such as speech recognition and natural language understanding, the development of specialized domain-specific translation models, increased focus on post-editing and human-machine collaboration, and the expansion of machine translation services into emerging languages and markets.
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