Semantic Data Annotation Services & Labelling for ML and Deep Learning

Get Your ML Training data to build better image, video, text and speech recognition with meaningful information that will be used to train and improve machine learning models.

Data annotation is the act of associating raw data—like text, images, audio, or video—with labels, allowing it to be used in training machine learning (ML) and artificial intelligence (AI) models. Data annotation is an integral aspect of supervised learning, allowing systems to identify patterns, process language, and make predictions. Nonetheless, this process of developing accurate and robust automatic image annotation models presents several daunting challenges. The acquisition of the relevant images and textual features to build valid annotation models present yet another hurdle.

At Statswork, our data scientists and consultant team design an end-to-end semantic data annotation and data labelling process through tagging for computer vision, pattern recognition, and machine learning solutions that empower high-powered A.I. and machine learning options such as convolution neural network.

We are experts in exactly labelling many different data products – images, text, audio, and video – with the help of automated tools, deep learning models, and humans. We provide quality, domain specific labelling for image object detection, facial recognition, text classification, video tracking, and pretty much any other data annotation and labelling application that is specific to your industry and your organization.

We take the time to work alongside your internal team and create sustainable partnerships to generate solutions that match your overall strategy. In healthcare, e-commerce, the automotive industry, or the finance sector, Statswork can help you build intelligent systems by providing accurate, consistent, and secure data annotation services. We will set the foundation for all your A.I. ambitions to succeed together.

The design of tasks that is planned well during both data collection and annotation is essential to machine learning models effectively learning and producing results that are consistent and reliable across settings and domains.

Accurate AI Performance is Powered by Data

The best data will deliver the best AI. Quality annotation and thoughtful task design during data collection and data annotation ensures your models generalize accurately and perform well over several applications

Improvements in Development Efficiency

More useful datasets mean cleaning, structuring and re-arranging takes away less time to train models. This increases development velocity and savings, while improving overall workflow efficiencies.

Your data becomes a competitive advantage

We help you think about custom annotation and how it will allow you to operationalize AI models that are force-multipliers for your specific domain or industry, or its context.

Improvements in Model Accuracy

Accurate annotations let machine learning models better see patterns, identify entities, and generate outputs that are more reliable and accurate.

At Statswork, we offer robust data annotation services that are specifically designed for feeding your AI or machine learning model with every possible data type. Our experts in the relevant subject matter guarantee high quality, accurate and value-add annotation across a variety of datatype so that you will achieve the tailored results for your important work.

Image Annotation

We can use, bounding boxes, polygons, key points, and semantic segmentation to annotate image objects, features, and specific areas of interest accurately. Our image annotation services provide your models with quality and precise visual data.

Text Annotation

We offer accurate text annotations for natural language processing (NLP) tasks, such as named entity recognition (NER), sentiment analysis, intent classification, and part-of-speech tagging. We achieve zero language errors and meaning-consistent,

Audio Annotation

We deliver tagging and segmentation for speaker and background noise, speaker and emotional identification for audio annotation. Our audio annotation services deliver transcription and labelling of audio datasets. Our transcription and labelling,

Video Annotation

With our video annotation service, we will annotate an object, action, or movement over time, providing labels for each frame. We help you to gain an understanding of dynamic scenes with proper temporal labelling and tracking of the object across frames.

Through the accurate, scalable, and domain-specific data annotation and labelling services, we support AI and machine learning applications. Here’s how our capabilities are unique:

Capability for Mixed Data Types

We have qualified annotators that can annotating both hard and soft data including images, videos, text, and audios which provides us the ability to work across the board for any AI training project.

Industry Expertise

Our annotators have specialized knowledge within certain sectors such as healthcare, life sciences, pharma, autonomous vehicles, retail and finance which ensures we can provide a much greater level of quality with respect to context and accuracy for a sector like labelling.

Scalable and Flexible

We can build teams to meet the needs of any dataset, whether your dataset is small or enterprise dataset. We work with flexible engagement models, and we can scale teams as needed to meet any project deadlines and we don’t have to compromise quality.

Human-in-the-Loop (HITL) Quality

We use a combination of automation and operators to create a human quality control process to a labelling project to provide precise annotation validated with quality control process.

Use of Annotation Tools

We support the annotation and labelling project with leading annotation platforms and AI led interfaces in workflows, with reduced manual effort to provide consistent continuous outputs.

Customized Annotation Processes

Our team can configure and/or amend annotation processes to the needs of the work it is supporting – e.g. bounding boxes, named entity recognition, sentiment, speaker.

Our data annotation and labelling solutions are unique to the industries in which we work, to fulfil the requirement of organizations adopting AI and machine learning to enhance operations, research, and decision making. Giving us the domain knowledge to provide accurate, scalable, and compliant annotations in the following industries:

Healthcare & Life Sciences

We offer high-accuracy annotation of medical images (e.g. X-rays, MRIs), clinical text, electronic health records (EHRs), and audio consultation files that enable diagnostic support tools, predictive analytics, and healthcare AI.

Transportation & Driverless Vehicles

We oversee the labelling of data for vehicles and drivers from various sensors and camera feeds, all to assist with image classification of lane and person detection, as well as real-time decision making and policy outcomes with an autonomous system.

Retail & E-commerce

We provide tagging of product images, customer sentiment, and relevance of searches for recommendations engines, visual searches, and user experience enhancements.

Pharmaceuticals & Biotechnology

We annotate biomedical literature, clinical trials, lab reports, and research studies so that AI can enable drug discovery, drug safety, and drug regulatory compliance.

Manufacturing & Industrial Automation

We produce visual data labels that help in defect detection, machinery monitoring, and automation systems to enhance efficiency and decrease downtime.

At Statswork, we have a well-defined and quality-centric data annotation process that is meant to deliver accuracy, efficiency, and uniformity. Our process melds domain knowledge, regulatory compliance, and scalable delivery to satisfy the near-infinite demands of AI and machine learning projects, across various domains.

Gathering Requirements

As planned the initial step is to gather the requirements for the project - project goals, data types, and specific information unique to the domain. Here we will scope the project, investigate the use case and desired formats of the annotation, so that we can establish standards and make sure we are aligned from day one.

Data Preparation

Your raw data (text, image, audio or video) will be cleaned and anonymized (if necessary) and prepared before being annotated by our team to verify the accuracy for your project. We confirm that your data is formatted and structured appropriately to fit with your intended annotation guidelines and machine learning goals.

Guidelines Creation

Consistency in the annotation process can only take place after setting up the guidelines based on your ultimate goals. We outline the structures of the labels, any metadata requirements, and create measures of quality based on the above - essentially standards to maintain quality.

Production

Your project manager will be responsible for your team of dedicated and trained annotators to ensure your project is labelled to a high quality and delivered on time. Quality comes first and is at the heart of our practices, that means we continuously monitor performance throughout.

Evaluation

In addition to continually checking quality, and receiving client feedback and its iteration, we QA the annotations and enhance them. Each dataset receives our layered QA and achieves the accuracy rates needed for your high-performing AI models

Final Delivery

At last, when your data clears all validation - it is ready for delivery, in whichever format you want (e.g., JSON, CSV, COCO). We are happy to help with future iterations or scaling

Thanks to the precise medical image annotation provided by the team, our AI model achieved clinical-grade accuracy. This directly contributed to our publication in the Journal of Medical Imaging and Health Informatics.

— CTO, HealthTech AI Startup,

- USA

We were impressed by the team's expertise in clinical text annotation. Their work helped us build an NLP pipeline that led to our successful article in the International Journal of Medical Informatics.

Lead Researcher, Clinical Research Organization,

- UK

The annotated dataset they delivered met all journal standards, and their adherence to HIPAA compliance was commendable. Our study was published in the BMC Medical Informatics and Decision-Making journal.

Principal Investigator, Healthcare AI Lab,

- Canada

The Statswork team helped us annotate and label a massive dataset for drug discovery, contributing to our manuscript accepted in Frontiers in Pharmacology. Their scientific accuracy was outstanding

Senior Scientist, Pharma Research Unit,

- India

Statswork is a group of data scientists, domain experts, and annotation specialists who produce high-quality data annotation and labelling services which drive AI and machine learning initiatives in numerous sectors.

We have a strong background in clinical research, life sciences, healthcare, and advanced analytics, which allows us to be compliant, precise, and scalable on every project we touch. We take all the necessary measures to ensure quality and domain accuracy, which is why organizations of all types choose us as their data preparation service when they are looking for accurately labelled, ethically prepared data.

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What is data annotation?

Data annotation is the process of labelling or tagging raw data—text, images, audio, or video—to make it consumable to train machine learning and AI models.

Why is data annotation important?

Data annotation quality is important because machine learning models “learn” relationships from labelled data to make predictions. If a data annotation is labelled correctly, it will result in AI technologies that are more accurate and reliable.

What types of data can be annotated?

Data annotation can be used to label and categorize examples of different types of data, such as:

Text: Sentiment analysis, named entity recognition, etc.
Images: Object detection, image segmentation, etc.
Audio: Speech recognition, speaker identification, etc.
Video: Action recognition, object tracking, etc.

What tools are commonly used for data annotation?

Examples of some of the more well-known data annotation tools include:

Labelling: An open-sourced tool for image annotation using bounding boxes.
Label box: A platform for data-layering collaboratively with different data types.
Amazon Mechanical Turk (MTurk): A crowdsourcing platform for outsourcing data-annotation jobs/tasks
Snorkel: A framework for programmatic creation of labelled datasets.

What are the challenges in data annotation?

There are challenges:

Annotation Quality: Ensuring consistency and accuracy across annotations.
Scalability: Annotating many datasets is time-consuming and often expensive.
Expertise: Sometimes labelling is technical or subject-matter specific and requires domain expertise.

How can I get started with data annotation?

Finally, you’ll be able to:

Understand the Basics: Learn good principles of machine learning and ai.
Annotate: Practice using open datasets to annotate.
Join Platforms: Join demand platforms like Amazon Mechanical Turk or Remotasks to find annotation tasks.

Is data annotation a good career option?

Data annotation can be a legitimate and flexible career or side tire for those looking to work with nonstandard work hours. However, it is important to be careful because some of the apps and platforms may have issues with task availability and account deactivation.

How is data annotation different from data labelling?

Those words usually have the same meaning; both refer to a tagging or defining the process for raw data with the purpose of having machine learning models understand it. Although the phrase “data labelling” is more commonly used in supervised learning contexts to describe labelling, “data annotations” may cover a wider range of actions.

What skills are needed for data annotation?

Essential competencies:

Attention to detail: Make sure the annotation is precise and accurate
Basic computer skills: Be comfortable using and familiarity with annotations and tools or platforms
Understanding AI/ML concepts: This is helpful in figuring out how to annotate
Patience and consistency: You will need to push through the repetition.

Can data annotation be automated?

There are some dimensions of data annotation that may be automated thanks to AI-controlled tools; however, human annotators are still necessary to ensure accuracy and to handle more complicated tasks, especially in specialized contexts.