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An umbrella term for all possible 3D annotations that cannot be placed under one of the other categories in the overview find their home here.

The process of converting speech in an audio file into written text and vice versa. This can be an: interview recording, academic research, political speech, news etc.

The most common kind of data annotation. These are rectangular boxes used to identify the location of the object. It uses x and y-axis coordinates in both the upper-left and lower-right corners of the rectangle. The prime purpose of this type of data annotation is to detect objects and locations.

Pose estimation is a computer vision technique that predicts and tracks a person's or object's location. This is accomplished by examining a given person's or object's pose and orientation which have been drawn by joining dots.

The process of categorizing or sorting images. These categories can be as broad or as specific as needed. Each image will be assigned to only one category. This can be used to train machine learning algorithms to improve ecommerce product discovery, image search engines, and concept recognition systems.

Instance segmentation identifies each time an object occurs. When there are more than one of the same object in an image then both will be given their own label and highlight colour

These two annotations are used to create dots across the image to identify the object and its shape. Landmark and key-point annotations play their role in facial recognitions, identifying body parts, postures, facial expressions and alike.

Officially part of 3D annotation. But because it has a specialist character, we have decided to give it a separate category. LIDAR is widely used in self-driving vehicles, but also in drones, automated harvesting vehicles and the like.

This type of data annotation detects and recognizes lanes, it is therefore mainly used for autonomous vehicles. But it also has applications in automation, where it is used to let robots place objects on a conveyor belt, for example.

Because many medical companies have specialized in this sector of AI, we have decided to give medical annotations a separate sector for better ease of use.

The process of recognizing information units such as names, including person, organization, and location names, and numeric expressions such as time, date, money, and percent expressions from unstructured text is known as named entity recognition (and classification.)

Concerned with the interactions between computers and human language, in particular how to program computers to process and analyze lanquage data. It is widly used in: chatbots, spamfilters, voiceassistants, grammarcorrection software and socialmedia monitoringtools.

Image labeling is a type of data labeling that focuses on identifying and tagging specific details in an image. It involves adding tags to raw data such as images and videos. Each tag represents an object class associated with the data.

Panoptic segmentation helps classify objects into two categories: things and stuff. Things. In computer vision, the term things generally refer to objects that have properly defined geometry and are countable, like a person, cars, animals, etc.

Polygonal segmentation is used to identify complex polygons to determine the shape and location of the object with the utmost accuracy. This is one of the more common types of data annotations.

This type of annotation finds its role in situations where environmental context is a crucial factor. It is a pixel-wise annotation that assigns every pixel of the image to a class (car, truck, road, park, pedestrian, etc.). Semantic segmentation is most commonly used to train models for self-driving cars.

Topic analysis is a Natural Language Processing (NLP) technique that allows us to automatically extract meaning from text by identifying recurrent themes or topics. Businesses deal with large volumes of unstructured text every day like emails, support tickets, social media posts, online reviews, etc.

Object detection in videos entails detecting the presence of an object in image sequences and possibly precisely locating it for recognition. Object tracking is the process of tracking an object. Such as its presence, position, size, shape, and so on.