Semantic Data Annotation Services & Labelling for ML and Deep Learning
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.
Image Annotation
Our text data collection can be leveraged for idea and product development, branding, shopping research that involves patients and experts, and clinical or market studies. Adding value-added depth of audience insight which aids in effective data solutions with AI and ML to meet the business challenges you face.
Receipt Data Collection
Train AI to identify many receipts such as invoices, bills, and multilingual variations while using next generation OCR to create all types of data sets.
Ticket Data Collection
Encourage machine learning model development for young travel tickets containing rich OCR text datasets.
EHR Data and Notes Transcription
Allow healthcare AI models to read and utilize medical records and note notes to produce clever clinical workflow automations.
Document Data Collection
Create intelligent models that recognize official documents like credit cards, property rights deeds, license, visas, and other proprietary paper forms.
Handwritten Data Transcription
Create AI tools to transcribe or transcribe and understand handwritten notes or historic documents with the fewest possible errors.
Intent Variation Data
Train natural language processing (NLP) systems on intent variation data that captures user intent, emotion, and various language problems.
Chatbot Data for Training
Use context to develop high-quality chatbot training text data for industry-specific and real-time conversational AI models.
OCR Data Collection and Model Training
Support the training of OCR model systems with trusted or reliable datasets for documents that contain image, text, and character recognition.Video Annotation
We collect all the text, audio, video, and image data from Facebook, Twitter, YouTube, and blogs—real user opinions, reviews, and interactions that train AI models with honest, social-driven data.
Social Media Posts
Information from social media posts (Facebook, Twitter, LinkedIn, Instagram) text, hashtags, responses, and user comments.
Online Reviews & Ratings
Feedback received through review sites (Amazon, Yelp, TripAdvisor and vehicle review sites) that contains opinions, sentiment, and satisfaction levels.
Forum & Community Discussions
Thread and response generated from “community” or forum-like sites (Reddit, Quora, and niche forums) that can capture patterns, issues, and public sentiments.
Blog & Article Comments
Responses and comments from users on media sites/blog posts to analyse user reactions, feedback, and engagement.
Visual & Other Media
Videos, images and audio that are shared publicly by users on sites like YouTube can be useful to support the design of multimodal AI systems.
Shopping Behaviour Data
Data from eCommerce platforms provides insight into users shopping behaviour as it relates to their preferences for products, purchasing decisions, shopping cart decisions, and comments left on reviews.
Chat & Messaging Data
Completes responses from user completion in customer service chats, chatbot, and user inputs from messaging apps to help train conversational AI.
Polls & Survey Responses (open-ended)
User submitted publicly available responses to open-ended questions on social media and surveys.Text Annotation
We collect all the text, audio, video, and image data from Facebook, Twitter, YouTube, and blogs—real user opinions, reviews, and interactions that train AI models with honest, social-driven data.
Social Media Posts
Information from social media posts (Facebook, Twitter, LinkedIn, Instagram) text, hashtags, responses, and user comments.
Online Reviews & Ratings
Feedback received through review sites (Amazon, Yelp, TripAdvisor and vehicle review sites) that contains opinions, sentiment, and satisfaction levels.
Forum & Community Discussions
Thread and response generated from “community” or forum-like sites (Reddit, Quora, and niche forums) that can capture patterns, issues, and public sentiments.
Blog & Article Comments
Responses and comments from users on media sites/blog posts to analyse user reactions, feedback, and engagement.
Visual & Other Media
Videos, images and audio that are shared publicly by users on sites like YouTube can be useful to support the design of multimodal AI systems.
Shopping Behaviour Data
Data from eCommerce platforms provides insight into users shopping behaviour as it relates to their preferences for products, purchasing decisions, shopping cart decisions, and comments left on reviews.
Chat & Messaging Data
Completes responses from user completion in customer service chats, chatbot, and user inputs from messaging apps to help train conversational AI.
Polls & Survey Responses (open-ended)
User submitted publicly available responses to open-ended questions on social media and surveys.Audio Annotation
We gather multilingual speech data from participants across the globe, to train voice-enabled AI; all while supporting projects regardless of size but doing so as quickly and accurately as possible.
Typical Conversation Speech
We are collecting natural and ideal real-world recordings of conversations with two or more speakers discussing a daily living topic for conversational AI training.
Call Centre Speech
We are collecting real-world recordings of customer service agents and customers while having calls to create audio data to train AI customer support models.
Wake Word Speech
We are gathering different samples of wake words in different languages and accents to train voice activation systems.
Voice Assistant Commands
We are collecting examples of voice commands in many dialects, languages, and accents to train AI voice assistants.
Scripted Monologue Speech
We are collecting recordings of spoken audio using scripted monologs with single speakers to provide consistent input to voice AI.
Image Description Speech
We are recording speech where speakers are simply describing images to audit into multimodal AI training for future AI models that combine visual and audio inputs.Industries We Serve – Data Annotation & Labelling
Raising Data Dictionary Mapping to Another Level with Intelligent Automation & Regulatory-Ready Integration
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.
1. 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.
2. 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.
3. 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.
4. 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.
5. Utilization 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.
6. 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.
"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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
