#Image Annotation in India

[priyanshilspl]

ADVANTAGES OF DATA ANNOTATION

Data annotation is essential for training AI models effectively. Precise labeling ensures accurate predictions, while scalability handles large datasets efficiently. Contextual understanding enhances model comprehension, and adaptability caters to diverse needs. Quality assurance processes maintain data integrity, while collaboration fosters synergy among annotators, driving innovation in AI technologies.

[10bmnews]

Nasa spacecraft around the moon photographs the crash site of a Japanese company's lunar lander - The Times of India

This image provided by NASA shows an annotation indicating the impact site for ispace’s Resilience lunar lander, seen by the Lunar Reconnaissance Orbiter Camera (Picture credit: AP) CAPE CANAVERAL: A Nasa spacecraft around the moon has photographed the crash site of a Japanese company’s lunar lander. Nasa released the pictures Friday, two weeks after ispace‘s lander slammed into the moon. The…

[rotzaprachim]

Author: 

Melonie Schmierer-Lee and Alan Elbaum

Wed 22 Jun 2022

Alan, which fragment are you looking at today?

My job description at the Princeton Geniza Project is to look at uncatalogued or minimally catalogued documentary fragments, and while looking for these I came across T-S NS J479, a single page covered with strange symbols written in all directions. I’ve probably glanced at around 50,000 Genizah fragments by now, and I’ve never seen anything that looks like this.

What is it? Which language is it?

Most of it is written in what I think is a made-up code, though whether it was invented or borrowed by the writer, I don’t know. There’s also some Arabic and Hebrew script (the Arabic is a petition formula). At first glance one of the symbols reminded me of one from the Voynich manuscript, so that set me wondering whether the symbols were meaningful. I noticed the same set of around 22 symbols all in a row, written a number of times, and wondered if the letters could be assigned to an alphabet. As there are roughly 22, the Hebrew alphabet fits better than Arabic. The language seems to be Judaeo-Arabic though. I’ve annotated an image of the fragment showing the ‘translation’ of the cipher into Hebrew script.

Why do you think he wrote out the alphabet several times?

Maybe he was trying to work out his alphabet. Towards the end he’s a bit inconsistent with some of the symbols assigned to each Hebrew letter, so perhaps he was refining it. He also writes the cipher alphabet from left to right at one point, which was interesting to me.

We keep saying ‘he’ – do we know who the author was?

He writes his name – ‘al-faqīr Isḥāq al-Yahūdī’ – as well as two verses from the revered Sufi poem known as Qaṣīdat al-Burda by Al-Būṣīrī (fl. 13th century), so that helps to date the fragment somewhat. Here are the lines in Stetkevych's translation (Suzanne Pinckney Stetkevych, The Mantle Odes: Arabic Praise Poems to the Prophet Muhammad (Bloomington, IN, 2010), p. 92.):Was it the memory of those you loved at Dhū Salam / That made you weep so hard your tears were mixed with blood? Or was it the wind that stirred from the direction of Kāẓimah / And the lightning that flashed in the darkness of Iḍam?

It’s Mamluk or perhaps Ottoman era. There’s also some pornography. I’ve learned two different words for penis and all sorts of other terms while studying the text. It’s fairly graphic. It ends ‘all of this is lies’, so perhaps Isḥāq was covering his tracks in case his parents cracked his code! Kind of frivolous but also kind of interesting.

Do you know of any other ciphers that have been found in the Cairo Genizah?

Gideon Bohak has written about at least one cipher that he’s found in the Genizah, and Oded Zinger has found a letter in Arabic and Judaeo-Arabic with a portion in an incomprehensible cipher. Almost all the words begin with alef, which makes us think it’s not a straightforward substitution cipher. Amir Ashur pointed out that some merchants in the India Book use Coptic numerals to create a secret code that hasn’t yet been cracked. I put this fragment up on social media after I started working on it, and people offered up all sorts of interesting parallels. Arianna D’Ottone-Rambach shared her article on an encrypted Quran manuscript that I hadn’t known about, for example. I’m so excited to join the field when this spirit of collaboration is recognised and valued. If I can make a discovery that lets someone else discover something further, then that’s all the better.

Thanks, Alan!

Alan Elbaum is a Senior Researcher at the Princeton Geniza Project.

[tangentiallly]

Despite uncovering widespread AI errors in healthcare, Ziad remained optimistic about how algorithms might help to care better for all patients. He felt they could be particularly useful in improving diagnostics that doctors tended to get wrong, but also in improving our current medical knowledge by discovering new patterns in medical data. Most modern healthcare AI is trained on doctors’ diagnoses, which Ziad felt wasn’t enough. ‘If we want AI algorithms to teach us new things,’ he said, ‘that means we can’t train them to learn just from doctors, because then it sets a very low ceiling – they can only teach us what we already know, possibly more cheaply and more efficiently.’ Rather than use AI as an alternative to human doctors – who weren’t as scarce as in rural India – he wanted to use the technology to augment what the best doctors could do.

[….]

To solve the mystery, Ziad had to return to first principles. He wanted to build a software that could predict a patient’s pain levels based on their X-ray scans. But rather than training the machine-learning algorithms to learn from doctors with their own intrinsic biases and blind spots, he trained them on patients’ self-reports. To do this, he acquired a training dataset from the US National Institutes of Health, a set of knee X-rays annotated with patients’ own descriptions of their pain levels, rather than simply a radiologist’s classification. The arthritis pain model he built found correlations between X-ray images and pain descriptions. He then used it to predict how severe a new patient’s knee pain was, from their X-ray. His goal wasn’t to build a commercial app, but to carry out a scientific experiment.

It turned out that the algorithms trained on patients’ own reported pain did a far better job than a human radiologist in predicting which knees were more painful.

The most striking outcome was that Ziad’s pain model outperformed human radiologists at predicting pain in African American patients. ‘The algorithms were seeing signals in the knee X-ray that the radiologist was missing, and those signals were disproportionately present in black patients and not white patients,’ he said. The research was published in 2021, and concluded: ‘Because algorithmic severity measures better capture underserved patients’ pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty.’

Meanwhile, Ziad plans to dig deeper to decode what those signals are. He is using machine-learning techniques to investigate what is causing excess pain using MRIs and samples of cartilage or bone in the lab. If he finds explanations, AI may have helped to discover something new about human physiology and neuroscience that would have otherwise been ignored.

— Madhumita Murgia, Code Dependent: Living in the Shadow of AI

[qualityavenuejellyfish]

A painting depicting the dodo ascribed to Ustad Mansur dated to the period 1628-33. This is one of the few coloured images of the dodo made from a living specimen.

Ustad Mansur (died 1624) was a seventeenth-century Indian painter and naturalist who served as a Mughal court artist. During which period he excelled at depicting plants and animals. He was the earliest artist to depict the dodo in colour, apart from being the first to illustrate the Siberian crane. Towards the end of Akbar's reign, he gained the title of ustad (master) and during the reign of Mughal Emperor Jahangir his masterpieces earned him the title of Nãdir-al-’Asr (Unequalled of the age). Although he was largely known for his natural history illustrations, he also portrayed people in various manuscript illustrations.

Ustad Mansur https://en.wikipedia.org/wiki/en:Ustad_Mansur (fl. https://www.wikidata.org/wiki/Q36424 circa 1590–circa 1630) wikidata:Q2502664

Description (English): Painting by the Mughal artist Ustad Mansur from c 1625, which may be one of the most accurate depictions of a live dodo. Two live specimens were brought to India in the 1600s according to Peter Mundy, and the specimen depicted might have been one of these. Other birds depicted are Loriculus galgulus (upper left) Tragopan melanocephalus (upper right), Anser indicus (lower left) (although the pose and pattern suggests a hybrid, possibly related to the Indian runner duck - note upright posture, long neck and smaller size although this is clearly not to scale going by the lorikeet) Pterocles indicus (lower right). Date: 17th century

Source/Photographer: Institute for Eastern Studies, Novo-Mikhailovsky Palace, Saint Petersburg. http://julianhume.co.uk/wp-content/uploads/2010/07/History-of-the-dodo-Hume.pdf Earlier version: http://www.natuurinformatie.nl/nnm.dossiers/natuurdatabase.nl/i005387.html

This work is in the public domain https://en.wikipedia.org/wiki/public_domain in its country of origin and other countries and areas where the copyright term is the author's life plus 100 years or fewer.

United States public domain tag https://commons.m.wikimedia.org/wiki/Commons:Copyright_tags/Country-specific_tags#United_States_of_America. This work is in the public domain in the United States.

[File:DodoMansur.jpg] Note: Metadata

This file has been identified as being free of known restrictions under copyright law, including all related and neighboring rights. https://creativecommons.org/publicdomain/mark/1.0/deed.en

Annotations: This image is annotated: View the annotations at Commons https://commons.wikimedia.org/wiki/File:DodoMansur.jpg

Chameleon by Mansur Mughal, c.1612

[paraprojects]

When AI Meets Medicine: Periodontal Diagnosis Through Deep Learning by Para Projects

In the ever-evolving landscape of modern healthcare, artificial intelligence (AI) is no longer a futuristic concept—it is a transformative force revolutionizing diagnostics, treatment, and patient care. One of the latest breakthroughs in this domain is the application of deep learning to periodontal disease diagnosis, a condition that affects millions globally and often goes undetected until it progresses to severe stages.

In a pioneering step toward bridging technology with dental healthcare, Para Projects, a leading engineering project development center in India, has developed a deep learning-based periodontal diagnosis system. This initiative is not only changing the way students approach AI in biomedical domains but also contributing significantly to the future of intelligent, accessible oral healthcare.

Understanding Periodontal Disease: A Silent Threat Periodontal disease—commonly known as gum disease—refers to infections and inflammation of the gums and bone that surround and support the teeth. It typically begins as gingivitis (gum inflammation) and, if left untreated, can lead to periodontitis, which causes tooth loss and affects overall systemic health.

The problem? Periodontal disease is often asymptomatic in its early stages. Diagnosis usually requires a combination of clinical examinations, radiographic analysis, and manual probing—procedures that are time-consuming and prone to human error. Additionally, access to professional diagnosis is limited in rural and under-resourced regions.

This is where AI steps in, offering the potential for automated, consistent, and accurate detection of periodontal disease through the analysis of dental radiographs and clinical data.

The Role of Deep Learning in Medical Diagnostics Deep learning, a subset of machine learning, mimics the human brain’s neural network to analyze complex data patterns. In the context of medical diagnostics, it has proven particularly effective in image recognition, classification, and anomaly detection.

When applied to dental radiographs, deep learning models can be trained to:

Identify alveolar bone loss

Detect tooth mobility or pocket depth

Differentiate between healthy and diseased tissue

Classify disease severity levels

This not only accelerates the diagnostic process but also ensures objective and reproducible results, enabling better clinical decision-making.

Para Projects: Where Innovation Meets Education Recognizing the untapped potential of AI in dental diagnostics, Para Projects has designed and developed a final-year engineering project titled “Deep Periodontal Diagnosis: A Hybrid Learning Approach for Accurate Periodontitis Detection.” This project serves as a perfect confluence of healthcare relevance and cutting-edge technology.

With a student-friendly yet professionally guided approach, Para Projects transforms a complex AI application into a doable and meaningful academic endeavor. The project has been carefully designed to offer:

Real-world application potential

Exposure to biomedical datasets and preprocessing

Use of deep learning frameworks like TensorFlow and Keras

Comprehensive support from coding to documentation

Inside the Project: How It Works The periodontal diagnosis project by Para Projects is structured to simulate a real diagnostic system. Here’s how it typically functions:

Data Acquisition and Preprocessing Students are provided with a dataset of dental radiographs (e.g., panoramic X-rays or periapical films). Using tools like OpenCV, they learn to clean and enhance the images by:

Normalizing pixel intensity

Removing noise and irrelevant areas

Annotating images using bounding boxes or segmentation maps

Feature Extraction Using convolutional neural networks (CNNs), the system is trained to detect and extract features such as

Bone-level irregularities

Shape and texture of periodontal ligaments

Visual signs of inflammation or damage

Classification and Diagnosis The extracted features are passed through layers of a deep learning model, which classifies the images into categories like

Healthy

Mild periodontitis

Moderate periodontitis

Severe periodontitis

Visualization and Reporting The system outputs visual heatmaps and probability scores, offering a user-friendly interpretation of the diagnosis. These outputs can be further converted into PDF reports, making it suitable for both academic submission and potential real-world usage.

Academic Value Meets Practical Impact For final-year engineering students, working on such a project presents a dual benefit:

Technical Mastery: Students gain hands-on experience with real AI tools, including neural network modeling, dataset handling, and performance evaluation using metrics like accuracy, precision, and recall.

Social Relevance: The project addresses a critical healthcare gap, equipping students with the tools to contribute meaningfully to society.

With expert mentoring from Para Projects, students don’t just build a project—they develop a solution that has real diagnostic value.

Why Choose Para Projects for AI-Medical Applications? Para Projects has earned its reputation as a top-tier academic project center by focusing on three pillars: innovation, accessibility, and support. Here’s why students across India trust Para Projects:

🔬 Expert-Led Guidance: Each project is developed under the supervision of experienced AI and domain experts.

📚 Complete Project Kits: From code to presentation slides, students receive everything needed for successful academic evaluation.

💻 Hands-On Learning: Real datasets, practical implementation, and coding tutorials make learning immersive.

💬 Post-Delivery Support: Para Projects ensures students are prepared for viva questions and reviews.

💡 Customization: Projects can be tailored based on student skill levels, interest, or institutional requirements.

Whether it’s a B.E., B.Tech, M.Tech, or interdisciplinary program, Para Projects offers robust solutions that connect education with industry relevance.

From Classroom to Clinic: A Future-Oriented Vision Healthcare is increasingly leaning on predictive technologies for better outcomes. In this context, AI-driven dental diagnostics can transform public health—especially in regions with limited access to dental professionals. What began as a classroom project at Para Projects can, with further development, evolve into a clinical tool, contributing to preventive healthcare systems across the world.

Students who engage with such projects don’t just gain knowledge—they step into the future of AI-powered medicine, potentially inspiring careers in biomedical engineering, health tech entrepreneurship, or AI research.

Conclusion: Diagnosing with Intelligence, Healing with Innovation The fusion of AI and medicine is not just a technological shift—it’s a philosophical transformation in how we understand and address disease. By enabling early, accurate, and automated diagnosis of periodontal disease, deep learning is playing a vital role in improving oral healthcare outcomes.

With its visionary project on periodontal diagnosis through deep learning, Para Projects is not only helping students fulfill academic goals—it’s nurturing the next generation of tech-enabled healthcare changemakers.

Are you ready to engineer solutions that impact lives? Explore this and many more cutting-edge medical and AI-based projects at https://paraprojects.in. Let Para Projects be your partner in building technology that heals.

[wisepl]

Powering the AI Revolution Starts with Wisepl Where Intelligence Meets Precision

Wisepl specialize in high-quality data labeling services that serve as the backbone of every successful AI model. From autonomous vehicles to agriculture, healthcare to NLP - we annotate with accuracy, speed, and integrity.

🔹 Manual & Semi-Automated Labeling 🔹 Bounding Boxes | Polygons | Keypoints | Segmentation 🔹 Image, Video, Text, and Audio Annotation 🔹 Multilingual & Domain-Specific Expertise 🔹 Industry-Specific Use Cases: Medical, Legal, Automotive, Drones, Retail

India-based. Globally Trusted. AI-Focused.

Let your AI see the world clearly - through Wisepl’s eyes.

Ready to scale your AI training? Contact us now at www.wisepl.com or info@wisepl.com Because every smart machine needs smart data.

[optomaindia]

Revolutionizing Classrooms with Optoma’s Interactive Panels

In the age of digital transformation, the way we teach and learn is rapidly evolving. Traditional chalkboards and static projectors are giving way to dynamic, tech-driven solutions that foster greater engagement and collaboration. At the forefront of this evolution is Optoma’s Interactive Panel for Classrooms��— a powerful, all-in-one solution designed to make teaching more impactful and learning more immersive.

What is an Interactive Panel?

An interactive panel is a large touchscreen display that allows teachers and students to write, draw, annotate, and interact with content directly on the screen. Unlike conventional whiteboards, these panels combine ultra-high-definition visuals, intuitive touch functionality, and integrated software to deliver a truly interactive experience.

Why Optoma’s Interactive Panels Stand Out

At Optoma, we understand that every classroom has unique needs. That’s why our interactive panels are engineered with educators in mind — offering cutting-edge features that promote collaboration, creativity, and flexibility.

1. Crystal-Clear Visuals

Optoma interactive panels boast 4K UHD resolution, ensuring that every image, video, and document appears sharp and vibrant. Whether it’s a complex diagram in a science class or a historical documentary in a social studies lesson, students enjoy a rich visual experience that supports better comprehension.

2. Intuitive Multi-Touch Technology

Our panels support multi-touch input, allowing multiple students to work on the screen at the same time. This promotes group collaboration and active participation — key elements in 21st-century learning.

3. Pre-Loaded Educational Tools

Optoma’s interactive panels come equipped with built-in educational software designed to make lessons more interactive and engaging. Teachers can use tools like digital whiteboarding, screen recording, and cloud integration to enhance their teaching methods effortlessly.

4. Seamless Connectivity

With a range of ports and wireless capabilities, our interactive panels easily connect to laptops, tablets, document cameras, and other devices. This ensures that content can be shared and displayed in real-time, without technical interruptions.

5. Eco-Friendly and Cost-Effective

Unlike traditional teaching tools that require consumables like markers, projectors, or paper, interactive panels are a one-time investment that reduces long-term costs and waste — making them a sustainable choice for educational institutions.

Transforming Classrooms Across India

From urban schools in Mumbai to rural learning centers in Tamil Nadu, Optoma interactive panels are making a measurable difference. Educators report higher student engagement, improved academic outcomes, and greater satisfaction with their teaching environments.

The Future of Learning is Interactive

As classrooms continue to evolve, the need for smart, interactive solutions becomes increasingly clear. With Optoma’s commitment to innovation and quality, our interactive panels are not just tools — they are catalysts for a new era of education in India.

Ready to upgrade your classroom? Visit www.optoma.co.in to explore our full range of interactive panels and discover how Optoma is redefining the future of education.

Read More Link:-

Business Projector

Optoma India

4K Projector

Projector For Home

[tagbintech]

Which is the Fastest Growing AI Company in 2025?

Introduction

The race for dominance in artificial intelligence (AI) has intensified, with 2025 marking a pivotal year. As industries increasingly rely on AI to automate, analyze, and innovate, one question resonates across global markets: Which is the fastest growing AI company in 2025?

This article explores the frontrunners, innovation metrics, global expansion strategies, and why one company is standing out as the fastest-growing AI force in the world today.

1. The AI Growth Explosion in 2025

2025 has witnessed unprecedented AI adoption in sectors like healthcare, finance, manufacturing, retail, and logistics. Governments, corporations, and startups are all racing to deploy intelligent systems powered by generative AI, edge AI, and hyper-personalized data algorithms.

Market reports project the global AI industry to surpass $500 billion by the end of 2025, with India, the U.S., and China contributing significantly to this growth. Within this booming ecosystem, several companies are scaling aggressively—but one has managed to eclipse them all.

2. Meet the Fastest Growing AI Company in 2025: OpenAI

OpenAI continues to lead the charge in 2025, showing exponential growth across sectors:

• Revenue Growth: Estimated to cross $10 billion, with enterprise AI solutions and API integrations leading the charge. • User Base: Over 1 billion users globally leveraging tools like ChatGPT, DALL·E, and Codex. • Enterprise Adoption: Strategic collaborations with Microsoft, Salesforce, and Indian tech companies. • AI Research Excellence: Introducing new models such as GPT-5 and Sora, dominating in NLP, computer vision, and video generation.

What makes OpenAI the fastest-growing AI company in 2025 is not just its innovation pipeline but its scalable infrastructure and deep integration into enterprise and consumer ecosystems.

3. Rising Contenders: Other Fast-Growing AI Companies

While OpenAI takes the crown, other AI companies are not far behind:

1. Anthropic

• Known for Claude 2 and 3 models. • Focuses on ethical AI and enterprise safety.

2. Tagbin (India)

• India’s leading AI innovator in 2025. • Powering smart governance, digital heritage, and cultural analytics with AI Holobox, AI dashboards, and immersive data storytelling. • Rapidly expanding across Southeast Asia and the Middle East.

3. Scale AI

• Powers autonomous vehicles and AI data annotation. • Secured major defense and logistics contracts in 2025.

4. Nvidia

• Surged with its AI GPU architecture. • AI infrastructure backbone for multiple AI startups globally.

4. Key Factors Behind AI Company Growth

The following attributes separate fast-growing AI companies from the rest in 2025:

• Innovation & Patents: Companies like OpenAI and Tagbin are leading in AI patents and deep learning breakthroughs. • Cross-Sector Applications: AI tools serving education, retail, agriculture, and governance are more likely to scale. • Strategic Partnerships: Collaborations with tech giants and governments. • Data Privacy & Ethics: Building trustworthy AI that complies with global standards.

5. India’s AI Growth Surge: The Role of Tagbin

Tagbin is the top Indian AI company accelerating the country’s AI ambitions in 2025. With high-impact solutions for smart governance and cultural transformation, Tagbin is emerging as a global AI thought leader.

Key achievements in 2025:

• Expanded operations to 10+ countries. • Launched AI-powered immersive storytelling platforms for tourism and heritage. • Collaborated with Indian ministries for AI-driven public engagement and analytics.

If growth trajectory continues, Tagbin could rival global leaders by 2026.

6. Market Outlook: What’s Next for AI Leaders?

By 2026, the fastest-growing AI company will likely offer:

• Unified multimodal AI models (text, image, video, voice). • Real-time learning systems. • Personal AI assistants for every profession. • Ethical compliance with AI laws worldwide.

Investors and developers are already tracking OpenAI and Tagbin as pioneers shaping the future of human-AI collaboration.

Final Thoughts

In 2025, OpenAI has emerged as the fastest-growing AI company globally, thanks to its groundbreaking products, enterprise-grade integrations, and visionary leadership. However, the AI landscape is far from static. Indian companies like Tagbin are rapidly closing the gap, offering localized, ethical, and scalable AI innovations that address both societal and business needs.

As we approach 2026, what defines the fastest-growing AI company won't just be revenue or user base—it will be impact, trust, innovation, and adaptability. For now, OpenAI leads, but the AI frontier remains dynamic, diverse, and full of surprises.

[global-research-report]

How Data Annotation Tools Are Paving the Way for Advanced AI and Autonomous Systems

The global data annotation tools market size was estimated at USD 1.02 billion in 2023 and is anticipated to grow at a CAGR of 26.3% from 2024 to 2030. The growth is majorly driven by the increasing adoption of image data annotation tools in the automotive, retail, and healthcare sectors. The data annotation tools enable users to enhance the value of data by adding attribute tags to it or labeling it. The key benefit of using annotation tools is that the combination of data attributes enables users to manage the data definition at a single location and eliminates the need to rewrite similar rules in multiple places.

The rise of big data and a surge in the number of large datasets are likely to necessitate the use of artificial intelligence technologies in the field of data annotations. The data annotation industry is also expected to have benefited from the rising demands for improvements in machine learning as well as in the rising investment in advanced autonomous driving technology.

Technologies such as the Internet of Things (IoT), Machine Learning (ML), robotics, advanced predictive analytics, and Artificial Intelligence (AI) generate massive data. With changing technologies, data efficiency proves to be essential for creating new business innovations, infrastructure, and new economics. These factors have significantly contributed to the growth of the industry. Owing to the rising potential of growth in data annotation, companies developing AI-enabled healthcare applications are collaborating with data annotation companies to provide the required data sets that can assist them in enhancing their machine learning and deep learning capabilities.

For instance, in November 2022, Medcase, a developer of healthcare AI solutions, and NTT DATA, formalized a legally binding agreement. Under this partnership, the two companies announced their collaboration to offer data discovery and enrichment solutions for medical imaging. Through this partnership, customers of Medcase will gain access to NTT DATA's Advocate AI services. This access enables innovators to obtain patient studies, including medical imaging, for their projects.

However, the inaccuracy of data annotation tools acts as a restraint to the growth of the market. For instance, a given image may have low resolution and include multiple objects, making it difficult to label. The primary challenge faced by the market is issues related to inaccuracy in the quality of data labeled. In some cases, the data labeled manually may contain erroneous labeling and the time to detect such erroneous labels may vary, which further adds to the cost of the entire annotation process. However, with the development of sophisticated algorithms, the accuracy of automated data annotation tools is improving thus reducing the dependency on manual annotation and the cost of the tools.

Global Data Annotation Tools Market Report Segmentation

Grand View Research has segmented the global data annotation tools market report based on type, annotation type, vertical, and region:

Type Outlook (Revenue, USD Million, 2017 - 2030)

Text

Image/Video

Audio

Annotation Type Outlook (Revenue, USD Million, 2017 - 2030)

Manual

Semi-supervised

Automatic

Vertical Outlook (Revenue, USD Million, 2017 - 2030)

IT

Automotive

Government

Healthcare

Financial Services

Retail

Others

Regional Outlook (Revenue, USD Million, 2017 - 2030)

North America

US

Canada

Mexico

Europe

Germany

UK

France

Asia Pacific

China

Japan

India

South America

Brazil

Middle East and Africa (MEA)

Key Data Annotation Tools Companies:

The following are the leading companies in the data annotation tools market. These companies collectively hold the largest market share and dictate industry trends.

Annotate.com

Appen Limited

CloudApp

Cogito Tech LLC

Deep Systems

Labelbox, Inc

LightTag

Lotus Quality Assurance

Playment Inc

Tagtog Sp. z o.o

CloudFactory Limited

ClickWorker GmbH

Alegion

Figure Eight Inc.

Amazon Mechanical Turk, Inc

Explosion AI GMbH

Mighty AI, Inc.

Trilldata Technologies Pvt Ltd

Scale AI, Inc.

Google LLC

Lionbridge Technologies, Inc

SuperAnnotate LLC

Recent Developments

In November 2023, Appen Limited, a high-quality data provider for the AI lifecycle, chose Amazon Web Services (AWS) as its primary cloud for AI solutions and innovation. As Appen utilizes additional enterprise solutions for AI data source, annotation, and model validation, the firms are expanding their collaboration with a multi-year deal. Appen is strengthening its AI data platform, which serves as the bridge between people and AI, by integrating cutting-edge AWS services.

In September 2023, Labelbox launched Large Language Model (LLM) solution to assist organizations in innovating with generative AI and deepen the partnership with Google Cloud. With the introduction of large language models (LLMs), enterprises now have a plethora of chances to generate new competitive advantages and commercial value. LLM systems have the ability to revolutionize a wide range of intelligent applications; nevertheless, in many cases, organizations will need to adjust or finetune LLMs in order to align with human preferences. Labelbox, as part of an expanded cooperation, is leveraging Google Cloud's generative AI capabilities to assist organizations in developing LLM solutions with Vertex AI. Labelbox's AI platform will be integrated with Google Cloud's leading AI and Data Cloud tools, including Vertex AI and Google Cloud's Model Garden repository, allowing ML teams to access cutting-edge machine learning (ML) models for vision and natural language processing (NLP) and automate key workflows.

In March 2023, has released the most recent version of Enlitic Curie, a platform aimed at improving radiology department workflow. This platform includes Curie|ENDEX, which uses natural language processing and computer vision to analyze and process medical images, and Curie|ENCOG, which uses artificial intelligence to detect and protect medical images in Health Information Security.

In November 2022, Appen Limited, a global leader in data for the AI Lifecycle, announced its partnership with CLEAR Global, a nonprofit organization dedicated to ensuring access to essential information and amplifying voices across languages. This collaboration aims to develop a speech-based healthcare FAQ bot tailored for Sheng, a Nairobi slang language.

 Order a free sample PDF of the Market Intelligence Study, published by Grand View Research.

[himanshupractice2]

BLOG 10| Pre Production| Environment References Collection | Pinterest, Flickr collections

In my Darbhanga Palace environment research, I cast a wide net—curating images from Pinterest, Flickr, Google Photos, various design blogs, and academic research papers—then distilled everything into a PureRef file. I hunted down real-life photographers’ captures of the ghats and the palace, zooming in on even the smallest carvings, weathered stone textures, lattice details, and dawn mist that breathes life into the scene.

Pinterest: Organized boards for architectural patterns, color palettes, and atmospheric moods, pinning close-ups of jali screens and palace façades.

Flickr: Scoured high-resolution user albums and Commons archives for wide panoramas of the Ganges at sunrise and intimate shots of ritual activity on the steps.

Google Photos: Explored shared albums by local photographers and vloggers to grab mobile-shot studies of Chunar sandstone textures and natural light tests.

Design blogs & PureRef tutorials: Incorporated best practices for annotating and clustering references—labeling groups like “Façade Details,” “Atmospheric Light,” and “Ritual Activity” directly on the canvas.

Research papers: Reviewed scholarly analyses such as Amita Sinha’s Ghats of Varanasi in India: The Cultural Landscape Reclaimed, which offers in-depth cultural and spatial context for the ghats, enriching my understanding of their historical layering and ritual significance.

By importing over 200 images into PureRef—each tagged and annotated—I now have a dynamic, hierarchical reference canvas that fuels every stage of modeling, texturing, and lighting with authentic visual intelligence.

Selected Bibliography (Harvard Style) Sinha, A. (n.d) Ghats of Varanasi in India: The Cultural Landscape Reclaimed. Available at: https://www.researchgate.net/profile/Amita-Sinha/publication/280051401_Ghats_of_Varanasi_in_India_The_Cultural_Landscape_Reclaimed/links/55a5683308ae5e82ab1fa04f/Ghats-of-Varanasi-in-India-The-Cultural-Landscape-Reclaimed.pdf (Accessed: 15 April 2025).

[web-technology-experts-india]

The Role of Data in AI Development: Web Technology Experts India

In the modern technology-centric world, Artificial Intelligence (AI) is fast changing the way companies do business, engage with customers, and make decisions. Behind the intelligent automation, smooth user experiences, and predictive analytics is one single element—data. AI is only as strong as the data it learns from, which makes data the foundation of all AI development.

Why Data Matters in AI

Data is the gasoline that fuels AI algorithms. Machine learning models, a branch of AI, utilize vast amounts of data to predict patterns and learn. From voice assistants to facial recognition software or personalized suggestions, all these capabilities function on large-scale data analysis.

There exist different forms of data that play a crucial role in AI creation:

• Structured Data: Well-arranged and straightforwardly readable, such as databases or spreadsheets.

• Unstructured Data: Complex and raw, comprising images, audio, video, and text.

• Semi-Structured Data: Partly organized formats such as XML or JSON.

Different applications of AI use various types of data. For example, natural language processing is based on textual data, while computer vision tasks require image or video data sets.

How Web Technology Experts India Leverages Data

In the emerging AI world, Web Technology Experts India understands that high-quality clean data is the key to successful digital solutions. Whether it is creating AI-based chatbots or adding intelligent search and personalization capabilities, our solutions are designed on robust data foundations. That is why Web Technology Experts is the top web development company in India that offers various web development services at affordable prices.

Our methodology involves:

• Building robust data pipelines

• Preprocessing and cleaning data for enhanced outcomes

• Data labeling and annotation for model training

• Using analytics to derive actionable insights

With this approach, we make certain that the AI aspects of our web solutions return real-time utility to businesses as well as users.

Data Ethics and Quality for AI

Good data quality and ethics are particularly challenging in the field of AI. Poor quality or biased data can result in poor predictions, bad decision-making, and less-than-stellar user experiences. That's why we pay extra attention to:

• Maintaining the accuracy and variety of data

• Applying data augmentation to add to training sets

• Removing duplicates and worthless information

Besides, data privacy is a paramount concern. Strict data protection measures are adopted by our AI initiatives, such as GDPR compliance and safe handling of user data. We anonymize and encrypt data to ensure transparency and build trust.

AI Success Depends on Data Strategy

AI doesn't operate on just data—it eats it. If AI is going to produce outputs, companies have to have an explicit data plan that involves valid collection, storing, processing, and analysis. Whether it's enhancing customer experiences through personalization or predicting demand in supply networks, data-fed AI delivers exactness and convenience.

Empowering Businesses with Smart Web Solutions

For those organizations looking to leverage AI, the right technology partner is important. As one of the best web development agencies, Web Technology Experts India not only provides well-designed, responsive websites but also incorporates cutting-edge AI features underpinned by robust data strategies.

We realize that small companies usually have a limited budget but require competitive digital solutions. This is why we provide the top website development for small companies that incorporates intelligent design, easy functionality, and AI tools such as chatbots, analytics, and recommendation systems—to provide small companies with the competition they need to expand.

Conclusion

Data is the foundation of Artificial Intelligence. Without well-processed, relevant, and clean data, AI systems are unable to perform efficiently. Whether you are a startup company or small company, with Web Technology Experts India as your digital partner which provides best website building for small business services, you have access to innovative web solutions that use data in a smart way to drive innovation, optimize performance, and future-proof your business in a rapidly changing digital world.

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TYPES OF IMAGE ANNOTATION

Image annotation involves labeling images with annotations such as bounding boxes, polygons, or keypoints to provide context for machine learning models. It enables tasks like object detection, segmentation, and facial recognition. Image annotation is essential for training AI systems to accurately understand and interpret visual data