Data Annotation Types and Tools: The Key to Improved Machine Learning

In today’s digital age, data is a crucial component of any business or organization. However, raw data alone is not enough to fuel machine learning (ML) algorithms, and this is where data annotation comes in. Data annotation is the process of adding convideod meaning to data, which can help ML models to better understand and interpret it.

Data Annotation

This blog post will discuss the importance of data annotation for machine learning. More so, it’ll also highlight the types of data annotation, top data annotation tools, and why you should consider outsourcing data annotation services.

How can Data Annotation Improve Machine Learning?

Data annotation is a powerful practice that can help make data more useful for training machine learning models. Moreover, it can help ML models with:

● Enhanced data accuracy: Annotating data with accurate and relevant labels can help increase the accuracy of ML models. This is especially important for tasks that require a higher degree of precision, such as an image or speech recognition.

● Increased efficiency: Data Annotation can help ML models learn faster, requiring less data and fewer training sessions to reach a certain level of performance.

● Better interpretability: Annotating data with clear, descriptive labels can help developers understand how an ML model is making its predictions. This can be especially useful for tasks like medical diagnosis or financial fraud detection, where it is important to understand the reasoning behind a model’s decision.

Types of Data Annotation

Data annotation involves adding labels or tags to data in order to classify or categorize it. This process is essential for several tasks in machine learning and artificial intelligence as it helps to train and improve the accuracy of algorithms. There are several different types of data annotation, each with its own characteristics and applications. Some of these are listed under:

● Bounding boxes: Bounding boxes are rectangular boxes drawn around an object in the image. They are typically used to annotate objects in images for object detection. The annotation includes the (x, y) coordinates of the top-left corner and the bottom-right corner of the bounding box, as well as the class label of the object contained within the bounding box.

● Semantic segmentation: Semantic segmentation involves labeling each pixel in an image with a class label. This can be used to segment objects in an image or to label different parts of an image (e.g., grass, tree, sky).

● Landmark and keypoint annotation: This involves marking specific points of interest on an image or video. These might correspond to important features of an object (such as eyes, mouth, and the nose of a face). Or they might be points of reference for alignment (like corners of a building). This type of annotation is often used for tasks like face recognition and object tracking.

● Lines and splines: Lines and splines are used to annotate linear or curvilinear features within an image. A line annotation consists of a start and end point, while a spline annotation consists of a series of control points that define the shape of the curve. These types of annotation are often used to mark features such as roads, rivers, or contours of objects.

● Polygonal segmentation: Polygonal segmentation involves dividing an image into regions using a series of interconnected lines or curves. Each region is labeled with a class label indicating the type of pixels within that region. This can be used to identify more complex shapes or objects that cannot be represented using bounding boxes or 3D cuboids.

● 3D Cuboids: 3D cuboids are used to annotate objects in the 3D space. These annotations include information about the size, orientation, and location of the object in the 3D space. This type of annotation is often used in tasks such as object detection and pose estimation.

● Entity annotation: Entity annotation involves labeling and identifying specific entities within an image or video, such as objects, people, and locations. It is often used in tasks such as object detection and image classification.

Top Data Annotation Tools

Data annotation tools play a crucial role in labeling and categorizing data, enabling algorithms to understand and interpret the information correctly. Here are some popular data annotation tools that can help make your data annotation tasks more efficient and effective.

● Prodigy:

Prodigy is a data annotation tool that can be used for a variety of tasks, including named entity recognition, text classification, and part-of-speech tagging. Prodigy allows users to participate actively in the annotation process by providing suggestions for annotations based on the model’s prediction. It also includes a number of built-in features for managing and organizing annotation projects.


● Active learning capabilities can improve annotation efficiency

● Built-in features for managing annotation projects.


● May have a steep learning curve for new users.

● Users may experience performance issues while working with large datasets.

● Visual Object Tagging Tool (VoTT)

VoTT is an open-source tool developed by Microsoft for creating labeled datasets for object detection and image classification tasks. It has a user-friendly interface and supports a variety of annotation formats, including bounding boxes, polygons, and points. VoTT also includes features for organizing and exporting annotations.


● Free to use

● Allows users to tag multiple objects in an image or frame

● Intuitive interface for image and video annotation


● Only supports video and image annotations, hence may not be suitable for

users working with other types of data.

● PDF Annotator

PDF Annotator is a tool used for adding comments, highlights, and other annotations to PDF documents. It is primarily used for editing and reviewing documents but can also be used for data annotation tasks such as transcribing texts from scanned documents or adding labels to images in PDFs.


● Supports a wide range of annotation types, including texts, shapes, and images.

● Allows users to collaborate with team members in real-time


● Primarily directed towards annotating PDFs and may not be suitable for other types of data

● Diffgram

Diffgram is a data annotation tool specially designed for comparing and annotating images and videos. It allows users to visualize differences between two datasets and add annotations to highlight specific points of interest.


● Intuitive interface for comparing and annotating data

● Can be used for identifying and correcting errors in datasets


● It is a paid tool. Hence may not be an option for users with a restricted budget.

Why Outsourcing Data Annotation Services is a Feasible Option for Businesses?

One of the major challenges most businesses face when it comes to data annotation is choosing the right tools. There are a wide variety of options available, ranging from free, open-source tools to expensive commercial software. It can be difficult for businesses to determine the best tool for their needs, as they may not have the necessary expertise or resources to properly evaluate and compare various options.

Another challenge comes with the cost associated with paid data annotation tools. Data annotation tools can be quite expensive, especially for businesses that need to annotate large volumes of data or require specialized features. This can be a significant burden for businesses, particularly small or medium-sized enterprises that may have a limited budget.

Data annotation services can help businesses overcome these challenges by providing professional and accurate data annotation services at an affordable cost. These services can help businesses choose the right data annotation tools for their needs and provide expert assistance in using these tools to annotate data accurately and efficiently.


Data annotation is an important part of the machine learning process and the right tools can make it faster and easier to build high-quality models. Understanding different types of data annotation and the available tools can help you choose the best solution for your machine-learning needs. However, if the process gets overwhelming for you, the best option is to outsource data annotation services to a reliable service provider. This will help you maximize your results and save some extra costs.

Author Bio

Jessica is a Content Strategist, currently engaged at Data-Entry-India.com- a globally renowned data entry, management and data verification company -for over five years. She spends most of her time reading and writing about transformative data solutions, helping businesses to tap into their data assets and make the most out of them. So far, she has written over 2000 articles on various data functions, including data entry, data processing, data management, data hygiene, and other related topics. Besides this, she also writes about eCommerce data solutions, helping businesses uncover rich insights and stay afloat amidst the transforming market landscapes.