Data labeling is growing faster than ever. Thanks to the advent of artificial intelligence, which positively shapes and assists large organizations’ machine learning models. Data labels augment the capabilities of AI-based systems in every domain. Human or software-labeled data helps machine algorithms accurately predict and define objects, text, images, or clips. It is possible through speech recognition, natural language processing (NLP), sentiment analysis, object detection, bounding boxes, and other advanced computer vision solutions.
What is Data Annotation?
Data annotation assigns tags or labels to unstructured data (still images, audio, video, or text) that provide contextual information to machine learning algorithms. It helps machines to read, understand, and interpret raw data, further enabling them to make precise estimations that impact business operations. Data labeling is a complex process that involves meticulous human skills and state-of-the-art AI technology that work in close coordination. After all, it requires the expertise of the highest level to tag unstructured data into meaningful information.
How does it work?
- The process begins with identifying raw data, such as audio recordings, video clips, or text files, and assigning meaningful and descriptive labels for each. It helps identify and categorize different objects, products, entities, sentiments, words, and relationships in a dataset.
- Data annotation can be performed by human annotators or using advanced software. Clients mostly prefer data labelling services by humans who manually tag raw data through intuitive thinking based on predefined guidelines and formats.
Benefits of Human Annotation or Labelling
Manual data annotation by humans ensures more accurate and precise tagging, enhancing the overall performance of AI systems and ML models across diverse business domains. A trained professional is in a better position to identify different objects, shapes, and sentiments that help convert raw data into machine-readable information.
Examples of Data Labels
- Image annotation by identifying objects or animals like cars, trucks, parked vehicles, dogs, cats, birds, etc.
- Identifying human sentiments in an audio recording by analyzing speech tone, use of words, voice, etc., which can be positive or negative.
- Identifying neutral or biased sentiment in a text by carefully analyzing the selection of different words, their parts of speech, punctuation, fillers, etc.
Top Data Annotation Companies
In the UK, US, Asia Pacific, and India, top data annotation companies, such as AyaData and others, have been ruling the roost for quite some time now. Other equally capable entities cannot be ignored at any cost. Global brand names like Cogito Tech, Quadrant Technologies, INFOLKS, Labelbox, etc., offer data annotation services for miscellaneous AI platforms and domains. Let us shortlist one name from the list and learn about its service offerings and expertise.
AyaData
AyaData has been in the business for years, catering to the needs of a diverse clientele. It offers a full spectrum of data annotation services for the healthcare, retail, agriculture, aviation, defense, homeland security, robotics, and geospatial AI-based segments. This company employs certified professionals and experts in 3D annotation, natural language processing (NLP), AI consulting, computer vision solutions, and whatnot. Whether image or text annotation, the quality of labeling is unmatched across domains.
Different Types of Data Annotation
- LiDAR Annotation
- Large Language Models (LLM) Annotation
- Image Annotation
- Video Annotation
- Text Annotation, and
- Multimodal Annotation
LiDAR Annotation: LiDAR (Light Detection and Ranging) annotation has redefined the scope of object identification. It uses Laser pulses to detect objects and measure distances. LiDAR plays a decisive role in modern-day applications, such as autonomous vehicles (self-driving cars), aerial objects, and drone distance mapping. Accurate distance is measured by sensors that classify objects within the 3D cloud point through image segmentation and drawing bounding boxes.
LLM Annotation: There are two main types of language model annotation. They are encoder-decoder models and transformer-based models. ChatGPT and AI-generated text software are the best examples of large language models that skyrocketed in popularity after 2022, which marked the end of the COVID era. Text-to-text translations via an input-output method and context understanding hold the key.
Image Annotation: It is one of the most prevalent data labelling services worldwide. It is a part and parcel of the computer vision annotation model. Image annotation helps machines recognize different objects and their relationships. Accurate labeling of images done by object detection, image segmentation, facial recognition, polygon annotation, and bounding boxes produces the desired results. It is helpful in applications like airport security systems and self-driving vehicles.
Video Annotation: Video annotation is the task of labeling motion pictures that assist machine learning algorithms in video analysis. Human annotators tag actions and behaviors within a video’s frame. It helps ML models interpret the underlying meaning of content by identifying the actions, emotions, feelings, and events. The task is executed through facial recognition, motion tracking, and action interpretation.
Text Annotation: Text tagging is one of the most widely used data annotation types in the AI sphere. Text annotation services add informative labels to text (words and sentences), making them machine-understandable and readable to machine algorithms. Text tagging or labeling ensures machines understand the nuances of human-written language by decoding the sentiment and semantic relationships. Machines can interpret the labeled datasets more accurately.
Final Words
If you are looking for accurate data annotation services in the UK, consider names like AyaData and others that have earned the trust of high-profile customers. They are among the few leading AI training data companies in Europe, helping large organizations and conglomerates gain seamless access to training data for machine learning and computer vision projects. View their official company website for more details.
