The global data annotation tools market size is expected to reach USD 3.4 billion by 2028. The market is expected to expand at a CAGR of 27.1% from 2021 to 2028.
The advent of big data is expected to drive the growth of the artificial intelligence market as a large volume of information is required to be recorded, stored, and analyzed. The adoption of artificial intelligence is expected to significantly boost market growth as the annotated information acts as a catalyzer to train AI models and machine learning systems in critical areas such as speech recognition and image recognition. These tools offer AI its strength by directly providing information that is relevant to determining future outcomes and decision-making.
Currently, there is a growing trend of manufacturing autonomous vehicles in the automotive industry, which is attracting larger investments for the development of these vehicles. An autonomous vehicle includes a combination of various sensors and networking systems that assist the computer in driving the vehicle. The annotated information allows autonomous vehicle computer models to recognize and learn from it. Several technology providers such as Google LLC; Tesla Motors; Apple Inc.; and Huawei Technologies Co., Ltd. have also entered the autonomous vehicle market. The rising investments in the self-driving market are expected to drive the future demand of the data annotation market.
Market Report Highlights
Rising demand for machine learning in automated data analytics is expected to augment demand for automatic information labeling tools in various data-driven applications. In addition, a rising focus on image annotation is anticipated to enhance the operations of the automotive, retail, and healthcare sector and thus is expected to propel market growth
In terms of annotation type, the manual segment captured the largest revenue share in 2020.The attributes such as accuracy, able to capture edge cases, and intelligent human resource is efficient enough to ensure high quality across large volumes of data. Which make manual annotation tools highly suitable to train machine learning algorithms for computer vision applications
Information labeling tools find greater acceptance in the automobile industry, especially for self-driving vehicles. High-resolution cameras, LIDAR sensors, and a huge amount of information are needed for creating the training data sets for such highly sensitive visual perception models in autonomous vehicles
Key Topics Covered:
Chapter 1 Methodology and Scope
Chapter 2 Executive Summary
Chapter 3 Data Annotation Tools Market Variables, Trends & Scope
Chapter 4 Data Annotation Tools: Type Outlook
Chapter 5 Data Annotation Tools: Annotation Type Outlook
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