Data Annotation and Labelling

What are data annotation and labelling services?

Data annotation and labelling services are like putting name tags on data so machines can understand them better. For example, if you want a machine to recognize different kinds of dogs in a photo, you need to label the dogs in the photo as "Golden Retriever", "Chihuahua", or "Poodle". This process, called data labelling, helps machines learn to recognize different objects and patterns in data.

Data annotation is the process of adding more information, like describing the colour or shape of objects in the photo, to help the machine learn even more.

Types of Data Annotation

Image annotation

Involves adding descriptive information to images, such as identifying objects, defining boundaries, and categorizing the image contents. This annotation type is useful for computer vision, object recognition, and image search applications.

Audio annotation

Involves adding labels or tags to audio data, such as identifying speakers, transcribing speech, and detecting background noise. Audio annotation is essential for speech recognition, natural language processing, and voice-based applications.

Video annotation

Similar to image annotation, video annotation involves adding descriptive information to video data, such as identifying objects, defining boundaries, and categorizing the video contents. This annotation type is useful for video analysis, object tracking, and action recognition applications.

Text annotation

Involves adding labels or tags to text data, such as identifying named entities, sentiment analysis, and topic classification. Text annotation is essential for natural language processing, text mining, and sentiment analysis applications.

Which businesses require these services?

Businesses that use computers to learn from data, like those in healthcare, finance, retail, transportation, and manufacturing etc., need data annotation and labelling services to help their machines learn. For example, a healthcare company might use data annotation to label different medical images so that their machine learning algorithms can identify signs of disease more accurately. Similarly, a transportation company might use data labelling to train their machines to recognize objects on the road, like pedestrians, bicycles, and cars. By using data annotation and labelling services, businesses can make their machines smarter and more accurate, which can help them save time, reduce costs, and make better decisions.

How can ProcessVenue help your business with data annotation and labelling services?

At ProcessVenue, we can help your business make the most of your data by providing expert data annotation and labelling services. Our experienced annotators use advanced tools and techniques to label and annotate your data accurately and consistently, so your machines can learn faster and more effectively.

By outsourcing your data annotation and labelling needs to us, you can save time and resources, improve the accuracy and consistency of your data, and get the most value from your data-driven insights.

Our Annotation and Labelling Services

Polygon annotation

Involves drawing a closed shape around an object or region of interest in an image or video. This annotation type is useful when the shape of the object or region is irregular or complex.

Bounding box annotation

Involves drawing a rectangle around an object or region of interest in an image or video. This annotation type is useful when the shape of the object or area is simple or rectangular.

Semantic segmentation

Involves labelling each pixel in an image with a corresponding class or category. This annotation type is useful for object recognition, scene understanding, and autonomous driving tasks.

Instance segmentation

Similar to semantic segmentation, instance segmentation involves labelling each pixel in an image with a corresponding class or category. However, instance segmentation distinguishes between different instances of the same style, such as identifying others in an image.

Audio transcription

Involves converting spoken words into text. This annotation type is useful for voice search, voice assistants, and call centre analysis tasks.

Sentiment analysis

Involves identifying the sentiment or emotion expressed in a text, such as positive, negative, or neutral. This annotation type is helpful for customer feedback analysis and monitoring social media.