Data annotation tools market is expected to witness tremendous growth in coming years. Audio and video annotation tools comprising of semantic annotation techniques and others are helping train AI and ML models. To train any ML-based model, the system needs to be able to detect any objects of interest in algorithm inputs right from the company name or person to accelerate output, as done by image annotation tools.
Majority traction across data management and analysis has resulted in adoption of AI solutions, with 9% UK firms followed by an 8% adoption in natural language processing and generation, 7% in machine learning, 5% in AI hardware and lastly in computer vision & image processing and generation which accounts for 5%.
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Technology has made its way in nearly every sector in the world, as is evident by the advent of artificial intelligence (AI), machine learning (ML) and other next-generation technologies. With massive volumes of data generated, stored, and transferred by the above-mentioned technologies, the need for data traffic management and data labelling has grown dominant.
Data annotation helps label a set of data, allowing the machine to identify and recognize the input material to provide an accurate output. Enterprises are growing more and more dependent on data annotation techniques and tools for highly accurate data results.
According to 2022 Capital Economics analysis for DCMS surveying AI activity in businesses across the UK, nearly 15% of all businesses — around 432,000 —¬ have adopted at least one AI technology whereas 2% (62,000) have newly integrated AI, and 10% —that is 292,000 — are pursuing at least one AI technology.
Considering the far-fetched scope for development, a report from Global Market Insights on data annotation tools market estimates that the industry size could surpass USD 10 billion by 2028.
India to foster growth of data annotation platforms
India’s ever growing digital ecosystem and technology market is offering lucrative opportunities for advanced systems. Some of the key barriers in Indian market include data privacy concerns, demand for other languages in labelling and cultural context.
COVID-19 in particular has dramatically driven adoption of AI fuelling economic recovery and shedding light on the importance of data assets and integrated systems.
Data annotation gained importance after a significant rise in AI penetration across the growing fields of India, including education, automobile, healthcare, e-commerce, and telecom.
The country also boasts of a very dynamic IT sector, which is capable of conceptualizing data annotation tools, both in-house as well as outsource to third parties, ultimately generating jobs catering to the nation’s burgeoning population with employment opportunities.
India is home to some of the breakthrough data labelling companies including iMerit, Zuru, Cogito Tech, Tika Data, and Wisepl. iMerit for example is a leading AI data solutions provider with high-quality data which facilitates machine learning and AI applications for Fortune 500 companies.
Annotation tools for medical imaging and unstructured texts
The healthcare sector has thus led to a surge in demand for annotation tools to deal with unstructured texts.
Additionally, data annotation techniques have also grown popular in medical imaging. Prevalence of AI and machine learning algorithms in the healthcare sector for accurate results in diagnostics such as medical imaging will lead to annotation tools showcasing innovative developments.
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Medical image annotation allows labelling of data derived through different medical imaging procedures such as ultrasound, MRI or CT scan.
In conclusion, ground-breaking technological development across healthcare, IT and telecom, automotive and other industries coupled with rapid adoption of online interface and digitally powered systems from a futuristic standpoint are expected to altogether foster growth of data annotation tools market realising the need for seamless, secure, and easily perceivable data.
Globally expanding healthcare sector as a result of strengthening infrastructure and next-generation technological advancements are transforming the face of medicine.
The healthcare industry generates stacks of clinical letters for documentation of details associated with patient consultation – comprising of patient history, diagnoses, and other valuable data. However, with unstructured format, these letters create barriers in retrieving and using the information for novel research.
Development in Natural Language Processing (NLP) techniques and gold standard annotation datasets can help with training and validation.