Data annotating is the labeling and categorization of data for A.I applications. With the growth of technology, a lot of developers are focusing on creating AI and ML models that are as human-like as possible.
To achieve this, training data is required to enable these models to process and understand specific information. Training data must be specifically annotated and labeled correctly for specific use. Data annotation services help IT companies to develop and improve AI.
Why Is Annotation Important?
- It improves the overall user experience.
- It makes the data collected actionable.
- It is used to develop and improve AI.
Categories of Data Annotation
Data is categorized into different groups. They are video, text, audio, and images.
Image annotation is essential for machines that use it to recognize an annotated area as a distinct object. It works by semantic segmentation and bounding boxes. Semantic segmentation is the assignment of meaning to every pixel.
Image annotation is used to increase accuracy and is used by a range of applications such as:
- Robotic vision
- Facial recognition
- Computer vision
- Self-driving vehicles
- Machines that pick and sort produce
Video annotation uses techniques such as bounding boxes on a frame-by-frame basis to acknowledge movement. Video annotation tools can also be used to serve the same purpose. The bounding of boxes on a frame-by-frame basis is to make them recognizable to machines.
Video annotation provides an in-depth visual perception that is used by autonomous vehicles to recognize the various types of objects on the road.
These are objects like street lights, other cars, pedestrians, signboards, traffic lanes, signals, and cyclists moving on the road. Video annotation, in essence, trains self-driving cars on how to operate on the road.
Data obtained from video annotation is important as it helps in object tracking. The data is also key for computer vision models that conduct localization.
An example of a video annotation tool is the Learning Spiral Suite, which has a variety of features to suit all your annotation needs.
Audio annotation is the time stamping and transcription of speech data. This includes dialect and language identification, speaker demographics, intonation, and pronunciation. Specific audio patterns can be used to enhance security and in hotline applications.
Sounds annotated include screeching, screaming, glass breaking, aggressive tones, and alarms.
Machine learning makes any speech or recorded audio recognizable to machines. This is important when developing applications such as chat boxes and virtual assistant devices.
Companies like Dialpad use audio annotation to improve by collecting audio, transcribing the dialogue, and using natural language processing algorithms to comprehend every conversation. Audio annotation data provided the training data needed to help the application run smoothly.
Text annotation is the most used data set. It includes a variety of annotations such as queries, sentiments, and intents. It is an activity that interacts with different texts to enhance the reader’s reaction and understanding of the text. This makes the sentences more meaningful.
Text annotations are important as they help machines recognize the crucial words in sentences and make them more meaningful. NLP makes it possible for machines to process and understand texts.
It also helps in making texts meaningful to machines controlled by artificial intelligence.
With the increasing number of human-machine interfaces, machines must be able to understand natural language and user intent. This helps in distinguishing between requests, commands, bookings, recommendations, and confirmations.
Semantic annotation helps with product listing and helps the customers to find the products they are looking for. It is important in training AI models to maintain and display relevant searches.
Annotation is an evolving field that continues to grow by the day. Thankfully, it continues to help in making data and AI models more accurate.