Image annotation
Image annotation ensures that machines perceive an annotated area as a different item. When such
models are trained, captions, identifiers, and keywords are added to them as attributes to images. The
algorithms then identify and understand these parameters and learn autonomously. It usually involves the
use of bounding boxes and semantic segmentation to be used in a range of AI-based applications like
facial recognition, computer vision, robotic vision, autonomous vehicles, among others.
Video annotation
Video annotation, like image annotation, uses techniques such as bounding boxes to recognize motion
frame-by-frame or using a video annotation tool. The data obtained from video annotation is essential for
computer vision models that perform object location and tracking. Video annotation allows seamless
implementation of concepts like location, motion blur, and object tracking, in the systems.
Audio annotation
Audio data comprises more dynamics like language, speaker demographics, dialects, mood, intention,
emotion, and behavior. Audio annotation requires identification of such parameters followed by tagging
using techniques such as timestamping, music tagging, and acoustic scene classification, among others.
Besides verbal cues, nonverbal instances such as silence, breaths, and even background noise can also
be annotated for a comprehensive understanding of the available audio file.
Text annotation
Text annotation is the process of assigning categories to sentences or paragraphs in a given document
based on the topic. This text can be anything, starting from consumer feedback to product reviews on
shopping sites, from a mention on social media to email messages. Since texts convey intentions in the
most straightforward way, there is a lot of scopes to derive useful information from them using text
annotation. The process of text annotation is a bit tricky and has a lot of stages because machines are
unfamiliar with concepts and emotions like fun, sarcasm, anger, and other abstract elements.
Semantic Annotation
Semantic annotation involves tagging concepts like people, places, or company names within a document
to help ML models categorize new concepts in the future text. It is a critical component of AI training to
improve chatbots and search relevance. Semantic annotation mainly involves tagging of key phrases and
the appropriate identification parameters; it has a crucial role to play in-text annotation.