Selbyville, Delaware, United States, October 7 2020 (Wiredrelease) Global Market Insights, Inc –:Global data annotation tools market growth is characterized by the seamless transition of raw unstructured data into insightful and useful Business Intelligence (BI) by machine learning facilities with human guidance, through the use of data annotation tools. Data annotation refers to the labelling of data in myriad forms, from video, audio, image, text, etc.
With regards to data type, the data annotation tools market is categorized into image/video, audio and text. Of these, the image/video data annotation tools industry from the polygonal annotation segment is poised to depict a CAGR of nearly 40% through 2026. This growth is ascribed largely to accurate object detection as well as image/video localization properties.
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Irregularly shaped or coarse object are difficult to detect for computer vision-based machines, unless they have been accurately labelled. Polygon annotation helps annotators to precisely plot the outlines of target objects, irrespective of their shape. Through this method, the exact edges of the object can be outlined, after which the pixels within the annotated outlines are tagged with the appropriate label to describe the object.
Computer vision and natural language processing-based artificial intelligence has numerous favorable applications, particularly in healthcare. The healthcare application segment is expected to account for nearly 20% of the total data annotation tools market share over the forecast period, owing to the robust use of the technology for medical image labelling.
Data annotation tools are used extensively in numerous medical disciplines including cardiology, pathology, oncology, radiology and even psychiatry, to detect patterns, facilitate diagnoses and establish treatment plans.
Data annotation in the medical sector involves recognizing disease or pathological markers for other medical properties, for instance in drawing outlines around tumors, designation ECG rhythm strips and pinpointing cells, among others.
With regards to the regional spectrum, North America held over 40% of the global data annotation tools industry share in 2019, as a result of rising prevalence of AI startups in the region.
To illustrate, Dataminr, an AI and machine learning startup based in New York, raised funding of over $392 million in 2018. The company is involved in providing comprehensive insights on information and news in real time.
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Likewise, Scale AI, a San Francisco-based startup dealing with data annotation closed a blockbuster funding round of $100 million in 2019. This expansion was made possible by the firm’s vast range of data annotation services, especially for autonomous driving clients including Waymo, Lyft, and the Toyota Research Institute.
TAB nan kontni:
Chapter 3. Data Annotation Tools Industry Insights
3.1. Entwodiksyon
3.2. Segmentasyon endistri
3.3. Jaden flè endistri, 2015 - 2026
3.4. Evolution of data annotation tools
3.5. Data annotation tools industry architecture
3.6. Data annotation tools industry ecosystem analysis
3.7. Teknoloji & inovasyon jaden flè
3.7.1. Pseudo labeling
3.7.2. Online content moderation
3.8. Regilye jaden flè
3.8.1. Amerik di Nò
3.8.1.1. NIST Espesyal Piblikasyon 800-144 - Gid sou Sekirite Sosyal ak sou enfòmasyon prive nan Cloud Computing Piblik (US)
3.8.1.2. Asirans Sante Portabilite ak Responsablite Lwa (HIPAA) nan 1996 (US)
3.8.1.3. Pwoteksyon Enfòmasyon Pèsonèl ak Lwa Dokiman Elektwonik [(PIPEDA) Kanada]
3.8.2. Ewòp
3.8.2.1. Jeneral Pwoteksyon Done Règleman (Inyon Ewopeyen)
3.8.2.2. Lwa sou Konfidansyalite Alman (Bundesdatenschutzgesetz- BDSG)
3.8.3. APAC
3.8.3.1. Teknoloji Sekirite Enfòmasyon- Espesifikasyon Sekirite Enfòmasyon Pèsonèl GB / T 35273-2017 (Lachin)
3.8.3.2. Tache Lend Nasyonal Digital Kominikasyon Policy 2018 - Draft (Lend)
3.8.4. Amerik Latin
3.8.4.1. Direksyon Nasyonal Pwoteksyon Done Pèsonèl (Ajantin)
3.8.4.2. Lwa brezilyen Jeneral Pwoteksyon Done (LGPD)
3.8.5. MEA
3.8.5.1. Lwa No 13 nan 2016 sou pwoteje done pèsonèl (Katar)
3.8.5.2. Federal Law No. 2 of 2019 on the use of ICT in Healthcare (UAE)
3.9. Fòs enpak endistri yo
3.9.1. Chofè kwasans
3.9.1.1. Rising demand for annotated data to improve machine learning models
3.9.1.2. Increasing investments in the development of autonomous driving technologies
3.9.1.3. Growing adoption of data annotation for medical imaging data
3.9.1.4. Surging uptake of text annotation for document classification
3.9.2. Enkonvenyans endistri yo ak defi yo
3.9.2.1. Inaccurate data labeling due to poor content quality
3.9.2.2. Lack of skilled professionals
3.9.2.3. High costs associated with manual data annotation
3.10. Kwasans analiz potansyèl yo
3.11. Porter’s analysis
3.12. PESTEL analysis
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