Data Annotation – Types, Tools, Benefits, and Applications in Machine Learning

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It is unarguably true that the advent of machine learning and artificial intelligence has brought a revolutionary change in various industries globally. Both these technologies have made applications and machines way smarter than our imaginations. But, have you ever wondered how AI and ML work or how they make machines act, think, and behave like human beings.

To understand this, you have to dig deeper into the technical things. It is actually the trained data sets that do the magic to create automated machines and applications. These data sets are further needed to be created and trained through a process named Data annotation.

Data annotation is the technique of labeling the data, which is present in different formats such as images, texts, and videos. Labeling the data makes objects recognizable to computer vision, which further trains the machine. In short, the process helps the machine to understand and memorize the input patterns.

To create a data set required for machine learning, different types of data annotation methods are available. The prime aim of all these types of annotations is to help a machine to recognize text, images, and videos (objects) via computer vision.

Types of Data Annotations

• Bounding boxes
• Lines and splines
• Semantic segmentation
• 3D cuboids
• Polygonal segmentation
• Landmark and key-point
• Images and video annotations
• Entity annotation
• Content and text categorization

Let’s read them in detail:

Bounding boxes

The most common kind of data annotation is bounding boxes. These are the 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 the objects and locations.

Lines and splines

This type of data annotation is created by lines and splines to detect and recognize lanes, which is required to run an autonomous vehicle.

Semantic segmentation

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.). Each pixel holds a semantic sense. Semantic segmentation is most commonly used to train models for self-driving cars.

3D cuboids

This type of data annotation is almost like bounding boxes but it provides extra information about the depth of the object. Using 3D cuboids, a machine learning algorithm can be trained to provide a 3D representation of the image.

The image can further help in distinguishing the vital features (such as volume and position) in a 3D environment. For instance- 3D cuboids help driverless cars to utilize the depth information to find out the distance of objects from the vehicle.

Polygonal segmentation

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

Landmark and key-point

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, and facial expressions.

Entity annotation

Entity annotation is used for labeling unstructured sentences with the relevant information understandable by a machine. It can be further categorized into named entity recognition and intent extraction.

Benefits of data annotation

Data annotation offers innumerable advantages to machine learning algorithms that are responsible for training predicting data. Here are some of the advantages of this process:

• Enhanced user experience

Applications powered by ML-based trained models help in delivering a better experience to end-users. AI-based chatbots and virtual assistants are a perfect example of it. The technique makes these chatbots to provide the most relevant information in response to a user’s query.

• Improved precision

Image annotations increase the accuracy of output by training the algorithm with huge data sets. Leveraging these data sets, the algo will learn various kinds of factors that will further assist the model to look for the suitable information in the database.

Formats of image annotations

The most common annotation formats include:

• COCO
• YOLO
• Pascal VOC

Applications of data annotations in machine learning

By now, you must be aware of the different types of data annotations. Let’s check out the applications of the same in machine learning:

• Sequencing- It includes text and time series and a label.

• Classification- Categorizing the data into multiple classes, one label, multiple labels, binary classes, and more.

• Segmentation- It is used to search the position where a paragraph splits, search transitions between different topics, and for various other purposes.

• Mapping- It can be done for language to language translation, to convert a complete text into the summary, and to accomplish other tasks.

Tools used for data annotations

Check out below some of the common tools used for annotating images:

• Rectlabel
• LabelMe
• LabelImg
• MakeSense.AI
• VGG image annotator

Final Words

In this article, we have mentioned what data annotation or labeling is, and what are its types and benefits. Besides this, we have also listed the top tools used for labeling images. The process of labeling texts, images, and other objects help ML-based algorithms to improve the accuracy of the output and offer an ultimate user experience.

A reliable and experienced machine learning company holds expertise on how to utilize these data annotations for serving the purpose an ML algorithm is being designed for. You can contact such a company or hire ML developers to develop an ML-based application for your startup or enterprise.

Read More: How does Machine Learning Revolutionizing the Mobile Applications?

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