Do you know? Artificial Intelligence has the potential to deliver an additional Global Economic
Activity of $13 trillion by 2030.

The foundation of AI and ML algorithms, data annotation, generates a highly accurate contextual
information that has a direct impact on algorithmic performance. For AI and ML models to recognize
and analyse in coming data accurately, annotated data is essential.

Worldwide spending on third-party data annotation solutions is anticipated to increase seven times
by 2023 compared to 2018, accounting for nearly one-fourth of all spending on annotation.

Large training dataset requirements, which are frequently specific to individual enterprises and
which data annotation services are addressing, are at the heart of the AI revolution.

Data Annotation: New Era of Data has just begun!
All machine learning and deep learning algorithms depend on data in some way. That is what drives
these smart and intricate algorithms to provide cutting-edge performances.

So, one must feed the algorithms with data which is appropriately structured and labelled if they
want to create AI models that are actually accurate. And this is where the Data Annotation process
makes an absolutely sense to the businesses.

Data must be annotated for machine learning algorithms to use it and to learn how to carry out
specific tasks.

Data Annotation – what is it?

This simply refers to marking the region or area of interest; this kind of annotation is unique to
photographs and videos. Apart from that, adding relevant information, like metadata, and
categorising text data are the main components of annotation.

Data annotation typically falls under the topic of supervised learning in machine learning, where the
learning algorithm links input with the relevant output and refines itself to minimise errors.

Types of Data Annotation

Image Annotation
The process of labelling an image is known as image annotation. It makes sure that an annotated
area in a given image is recognised by a machine learning system as a certain object or class.

1. Bounding box: Drawing a rectangle around a specific item in an image is known as “bounding.” Bounding boxes’ edges should contact the labelled object’s furthest pixels.
2. Object Detection: It can be used to annotate items that need to be grasped by a robot, such as those on flat planes that need to be navigated, like cars or planes.
3. Polygons: Users can make a pixel-level mask around the intended object which is why polygons are
   useful.
4. Semantic Segmentation :The process of grouping comparable parts or pixels of an object in an image is known as semantic segmentation. This method of annotating data enables the machine learning algorithm to learn and comprehend a particular feature and can aid in the classification of anomalies.

Best Use Case Scenario Data Labeling and Annotation

Rise of Virtual Assistants: Just like Alexa and Siri, developing next-generation personal assistants
involves a lot of text annotation. This is necessary because there are so many subtleties in human
speech that the annotators must label every piece of textual material to aid the system in
understanding them.

Increasing Crop Yield: With data annotation, now farmers can find the parts of the farmland that
needs more cultivation with the aid of drones that are driven by computer vision technology. For
farmers to make the most of their available farmland in order to successfully yeild crops.

Robotic Process Automation: A lot of the repetitive tasks that are performed in factories, farms,
warehouses, and other industries can be automated to relieve some of the workload from human
employees. However, in order to see and interact with the physical environment around them, these
robots depend on LiDAR and 3D Point Clouds.

Development of Autonomous Vehicles: AI vehicles are taught using a variety of image and video
training sets, which call for data annotators to label different aspects of the images. Simple examples
include drawing a bounding box around another object, or more complex examples include semantic segmentation, LiDAR, and 3D point cloud labelling.

Wondering how to start with Data Labeling Service?
In comparison to insourcing or in-house annotation, outsourcing data annotation has proven to be
both commercially and technically superior. In fact, a report claims that considering the
infrastructure, expertise, and employment costs associated with it, in-house data annotation is likely
to prove four to five times more expensive than outsourcing.

Outsourcing also means a stronger professional dedication and greater scalability. Additionally, it
includes a higher level of professional experience and expertise as well as significant and long-lasting cost savings from ready infrastructure without having to pay for hiring costs.
Data Labeler, specializes in creating quality labeled datasets for machine learning and AI initiatives.
Want to know how? Contact us!