The — data annotation tools market revenue is likely to cross USD 7 billion by 2027. Data annotation tools are steadily being adopted by healthcare facilities for classifying medical literature and for medical image labeling. The importance of correctly labeled textual data for the development of innovative technologies such as text-to-speech, voice recognition, and natural language generation is on the rise.
There is a substantial adoption of manual data annotation tools as well credited to better quality of labeled data than automated data labeling. Highly skilled professionals having strong expertise in the content and nature of data are required to perform manual data labeling. Human annotators are also able to handle and detect even small discrepancies in the input data, which are usually missed by automated data labeling tools.
Data annotation tool providers such as Google LLC, Amazon Web Services, Inc., IBM Corporation, CloudApp. Inc., and Clickworker GmbH are working on long term contracts and strategic collaborations with clients, as well as on new data labeling solutions and geographical expansions.
In 2019, Innodata Inc. announced the launch of its new expertly managed data annotation and labeling services for enterprises that need complex training data for artificial intelligence and machine learning models.
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Mentioned below are some of the ongoing trends in the data annotation tools market:
• Rising adoption of text annotation tools
The text data annotation tools category is anticipated to grow over 30% during the forecast period. Textual data labeling has attained significance as it is used for the development of document classification algorithms and performance of sentiment analysis. Social media text mining is another area where textual data annotation is becoming crucial as national security authorities and federal agencies monitor and check the behavior of citizens for probable terrorist threats. Security agencies are able to monitor objectional material in social media handles and potential threat topics by using deep learning neural networks embedded with text data annotation solutions.
• Growing implementation in agriculture industry
The demand for data annotation tools in the agriculture sector is expected to rise exponentially in the coming years credited to the surge of artificial intelligence in precision agriculture. Enterprises are able to create computer vision models which are used for several agricultural processes like crop monitoring, soil quality assessment, livestock monitoring, crop harvesting, and pest & disease control, with the help of data labeling solutions.
• Europe to amass major industry foothold
The Europe data annotation tools market is likely to grow to almost 30% owing to the wide adoption of AI technologies by regional business corporations. Europe has a mature artificial intelligence industry with many countries like the Netherlands, the Nordics, and Germany leading in the usage of machine learning applications. The European Commission has given priority to the importance of machine learning in a bid to enhance the region’s digital economy with a specific allocated budget for AI related projects. In 2021, Congenica, UK-based software company that makes modular and scalable data analytics tool for annotating, entered into a partnership with Camtech Diagnostics to distribute its software in Singapore, Japan, Malaysia, and South Korea.
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