What is data annotation?

Data annotation is the process of labeling or tagging relevant information/metadata in a dataset to let machines understand what they are. The dataset could be in any form, such as image, an audio file, video footage, or text. When we label elements in data, machine learning (ML) models accurately comprehend what they are going to process and keep that information to automatically process newer information that is built on existing knowledge to take timely decisions.

It is inevitable because AI and machine learning models need to be trained constantly to become more efficient and effective in delivering required outputs.

Why to annotate data?

Most of the data that is available today is unstructured. It is not defined properly or correctly. When building an AI model, it is necessary to feed the correct information into the algorithm to analyze and deliver the desired results and inferences.

This process can only occur if the algorithm understands the data you feed. It will allow the algorithm to classify the data accordingly.

The process of getting data in a position easily understandable for the ML algorithms is known as data annotation. It entails attributing, tagging, or labeling data accordingly so the ML and AI projects can understand it.

In a nutshell, data labeling and annotation is tagging the appropriate details/information in the dataset. Doing so will allow the machines to understand and use the data set accordingly. Also, the data set can be in any form, such as images, videos, audio, text, etc.


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What are the benefits of data annotation?

  • Businesses can improve customer interactions with chatbots and voice assistants, providing a more human-like conversation. This also leads to higher-quality results for search queries.
  • In-home IoT devices can detect everything from a human voice to a sudden movement in the home, which improves accessibility and home security.
  • Online videos, images, and articles have become increasingly accessible for users who have vision or hearing impairments.
  • Speech recognition technology has increased the range of accessibility on mobile and desktop devices as well.

Role of Data Annotation in Machine Learning and Artificial Intelligence

Labeling components in data will allow the ML models to comprehend precisely the information they will process. The models will also store the previous information to automatically process the new detail built on existing knowledge to take timely decisions.

In addition to comprehending data, the data annotation process also helps AI and ML models to know if the data set they receive is a visual, audio, image, video, text, or a combination of formats. Next, the model will classify the data and perform task execution according to your assigned features and parameters.

Data annotation is unavoidable since AI and ML models and projects need consistent training to provide more effective and efficient outcomes. This process becomes essential during supervised learning since the more annotated data you feed to the model, the more quickly it will start to train itself without help.

Let’s take about self-driving cars as an example; the automobile relies on data coming from different tech elements, such as:

  • Sensors
  • Other tech elements
  • NLP (Natural Language Processing)
  • Computer vision

The algorithms in these tech elements use data annotation to allow the vehicle to take precise driving decisions at every point. If not, the AI and ML model won’t understand whether the approaching obstacle is a person, animal, or another vehicle.

Therefore, without data annotation, the AI model’s results would provide unfavorable outcomes. The implementation of the data annotation will allow you to train your AI models precisely. As a result, you will get a complete model that will give you desired results whether you are deploying the model for speech recognition, chatbots, or any other process.

How does data annotation help improve machine learning models?

Data annotation helps improve machine learning models by providing them with more accurate and relevant information.

The supervised machine learning model is a type of algorithm that requires a pre-determined set of training data, which contains the correct answer or output for a particular problem. The model “learns” how to solve the problem by comparing this training data with the results it produces when applied to new, unlabeled raw data.

If the training dataset is not properly labeled, then there is a risk that the model will not learn how to correctly solve the problem. Data annotation helps ensure that all of the data in a dataset is accurately labeled so that the supervised machine learning model can learn from it effectively.

Machine learning models require both human and machine intelligence which is called a human-in-the-loop model.

Types of data annotation

Data annotation is a broad practice that encompasses different types of data, including image, text, audio and video. Each type of data has its own unique challenges when it comes to annotation.

Illustration of common data annotation types

data annotation types

Image Annotation for Computer Vision

Image annotation involves creating bounding boxes (for object detection) and segmentation masks (for semantic and instance segmentation) to differentiate the objects of different classes. Image annotation is often used to create training machine learning datasets for the learning algorithms

Text Annotation

This kind of annotation is the addition of relevant information about the language data by adding labels or metadata.
Learn more about text annotation for Machine Learning


Audio Annotation

Audio annotation is the process of recording and transcribing speech, with a focus on phonetics, accents, and speaker demographics. Every use case is different; some require a very specific approach such as tagging aggressive speech indicators for emergency hotline technology applications. The term “data annotation” can refer to anything from annotating the content of an audio file to annotating a single word. Several factors affect how efficient a system is for processing information, and data annotation helps with this process by identifying them all. Non-verbal cues such as silence or background noise are also annotated in order to make algorithms more efficient.

Video Annotation

Video annotation is the task of labeling sections or clips to be used to identify, classify, or detect the desired objects in a virtual environment. This is done using the same techniques as image annotation like bounding boxes or semantic segmentation, but on a frame-by-frame basis. Annotation is an essential technique for computer vision tasks such as localization and object tracking. By annotating videos, we can provide valuable information that can be used to improve these tasks.

Semantic Annotation

Semantic annotation refers to adding tags to various concepts, such as people, organization names, places, etc., in the document. This will assist ML models in dividing the new concepts in the future text into appropriate categories.

This annotation is essential for AI and ML training to enhance Chabot’s capabilities and improve search relevance. The semantic annotation usually includes tagging key phrases and the correct identification parameter.

What Things do You Need to Keep in Mind for Data Annotation in Machine Learning?

Now that you have a clear idea about data annotation and why it’s necessary for your ML projects, the next important thing is using it properly. If you want to make the most out of the data annotation, you need to see it as a part of your ML workflow.

It will require you to come up with a mixture of software elements, algorithms, annotators, etc. Also, you need to ask two questions for your data annotation project:

  • How are you going to utilize the limited resources for data annotation effectively?
  • How will you assess the quality of your annotations?

You can use various techniques to address these problems. To give you a better idea, we’ll give an overview of the two most effective techniques, which are :

Active learning tells you the methods to sample the data for annotation, whereas quality assessment is about validating annotation performance. Let’s discuss them in more detail.

Active Learning: Ways to Sample Data for Annotation

Active Learning refers to choosing data samples while keeping the point of data annotation at the forefront.

Since the resources necessary for the data annotation are scarce, you must utilize them effectively. You can choose from different types of active learning for data annotation. It can help you save time and money. Below are the three most common ones that most people use.

Diversity Sampling

Diversity sampling refers to the general paradigm that attempts to discover underrepresented or unknown values in your model. It can come in handy when you have to choose from various options. It is also known as:

  • Stratified Sampling
  • Representative Sampling
  • Anomaly or Outlier Detection

One of the key advantages of using this tool is that it can allow the model to learn about underrepresented information and details. In some cases, ML models will ignore certain information in the datasets due to their low occurrences. However, diversity sampling allows them to learn about such models.

Additionally, diversity sampling helps you avoid performance loss because of data drift. It usually happens when the AI or ML model contains too much data from the old and inaccurate sample regions.

Uncertainty Sampling

Uncertainty sampling refers to the process of choosing unlabeled samples that are close to the decision-making capabilities of the model. The benefit of this method is that you can identify samples with a higher possibility of being wrongly classified. So, you can manually annotate them to mitigate the chances of any errors.

Random Sampling

Lastly, random sampling is also a kind of active learning, and it is the simple one you can use. But the one challenge you might face is finding a random sample might be easy due to the distribution of the received data. Also, there are specific issues that you can catch with other methods but not with random sampling.

Quality Assessment: Validating Annotation Performance

Once you complete the sampling step, the next thing to do so is a proper QA. There is a chance the annotators can make mistakes or fail to identify any possible drawbacks. Therefore, introducing proper checkpoints to spot these mistakes is vital. Below we have covered some points to help you enhance the annotation performance:

  • Have Annotators with the Right Expertise: Having experienced annotators and experts can offer you high-quality details and can do the final reviews.
  • Dedicate a Team: It is better to have experienced people working on the project. It will increase the annotation accuracy and ensure everyone is on the same page regarding the relevancy.
  • Diversification: Getting people from different backgrounds, abilities, and expertise will ensure no systematic bias.

We have listed the four best practices for handling the quality assurance process to make things easy. Bloomberg’s Global Data department collects these practices. Here is a table explaining the quality assessment methods, their benefits, and their drawbacks.





Random QA

Sample randomly for AQ

  • You can review massive quantities of annotations
  • It does not need any follow-up discussions or preparation
  • Won’t be able to focus on possible errors

Gold task

Get the work items ready and compare them directly with the annotation “answer keys.”

  • Provides you with instant feedback with quantifiable outcomes
  • It only applies “objective” answer types
  • It needs some preparation work

Annotation Redundancy with Targeted QA

Conduct various annotations and do a proper QA on disagreeing results

  • It doesn’t require any preparation
  • Pinpoints the peculiarities
  • The feedback loops are quite longer
  • It also has a higher annotation time

Annotation Redundancy with Debrief

Conduct various annotations and discuss the guidelines that annotators apply

  • It doesn’t require any preparation.
  • Can identify subjective data with a variety of possible answers
  • Debrief takes quite some time
  • The feedback loops are quite longer

Data Annotation Process

When it comes to machine learning (ML), data annotation is an essential part of the process. It helps to clarify and understand the input patterns so that the system can learn from them and arrive at the desired outputs. The analogy of using flashcards to teach children is a good way to understand the concept. A flashcard with the picture of an apple and the word “apple” would tell the children how an apple looks and how the word is spelled. In data annotation, the label is the information that is added to the dataset for the machine learning model to understand and learn from.

The data annotation process can be time-consuming, but it is important to get it right. The more accurate the annotations are, the better the machine learning model will be able to function. As with anything else, practice makes perfect, so be sure to annotate your data as accurately as possible.

Automated data annotation and data annotated by humans

There are two main ways to annotate data: automated and human. Automated annotation is performed by machines, while human annotation is done by people. Both have their pros and cons:

Automated annotation can be faster and cheaper than human annotation, but may lack accuracy. This is because machines do not always correctly identify all the features of a dataset.

Human annotation is often more accurate, but also more costly. This is because humans are able to look at data in more detail and identify features that machines may miss. Additionally, human annotations can be checked for accuracy, which improves the quality of the data set overall.

How can I get started with data annotation?

The best way is to use an end-to-end toolset like Plainsight’s vision AI platform. This platform allows team collaboration, labeling instructions, dataset version control, AI-powered data annotation, and even no-code model training.

Another option for data annotation is iMerit. This company combines predictive and automated annotation technology with world-class customer service.

What are some best practices for data annotation?

There are a few best practices to keep in mind when it comes to data annotation:

  • Introducing a different data ingestion pipeline – This can help reduce the time it takes to get your data into a format that is ready for analysis.
  • How data is stored – When you store your data in a way that makes it easy to access and use, you’ll save time and effort later on.
  • Output format – Make sure that the output of your annotation process is in a format that is easy for you to work with.
  • Use of a new tool – If you’re introducing a new tool into your workflow, make sure that everyone who needs to use it is adequately trained.
  • Your workforce provider’s technology – Use the technology provided by your workforce provider to track the quality and productivity of its workers, and how they capture the data required to do it.

What tools are available for data annotation?

There are a variety of tools and methods you can use. You can either develop annotation tools in-house or use a commercial tool.

Developing annotation tools in-house is a good option for companies at the growth or enterprise stage. These tools can be customized with few development resources of your own. However, it’s important to create long-term processes and stack integrations that will meet your needs in terms of security and flexibility to make changes over time.

A few years ago, most data annotation tools were only available via open source or by building them yourself. However, in 2018, a number of commercial data annotation tools became available. These third-party, professionally developed tools offer full-featured, complete-workflow options for data labeling.

If you’re considering purchasing a data annotation tool, select one that meets the needs of your project in terms of security and flexibility.

Data annotation tool requirements

When looking for a data annotation tool, it is important to consider the following:

  • Strategic approach. That means it should be able to help with the overall annotation project and not just specific tasks.
  • Key features. For example, it should support machine learning as well as other annotations like text, audio, and video.
  • Secure and compliant. It must meet all security requirements and adhere to compliance regulations.
  • Quality control and assurance mechanisms in place. This ensures that all annotations are accurate and of high quality

What are some common challenges in data annotation?

One of the most common challenges is accurately labeling data. This can be difficult due to the time-consuming nature of the task and the need for precise labels.

Another challenge is ensuring that all data is accurately labeled. This can be a challenge due to variations in image quality and object size.

Finally, it can be difficult to find people who are skilled in data annotation.

Who can help me with annotation services?

If you’re looking for someone to help you with data annotation, clickworker is a great option. We have a platform that allows people from all over the world to sign up and work on projects and we have expertise in a variety of fields, including data annotation.

Annotation Services by clickworker

Clickworker provides annotation services for all types of data. All services are provided by a team of experts who have years of experience in the field. Data security is guaranteed with a reliable information security management system (ISMS) based on the ISO 27001 standard. Complete teams are available, including specialists for all business needs. Multilingual support is available for customer care service reps. Prices are affordable, and small and large projects are welcome. For any further questions do not hesitate to contact our Service Team.

Data Annotation – FAQ

Find answers to the most frequently asked questions on annotation.

What is data annotation or data labeling?

Data labeling is the process of adding labels to data points in a dataset. Data annotation, on the other hand, refers to describing each data point that falls within a specific range such as age or gender.

What is annotated data?

Annotated data is a collection of information about the high-level structure and semantics of a document or corpus. It’s typically unstructured text, but can also be semi-structured data. Annotations are a key component of text categorization, natural language processing and machine learning.

What does a data annotation specialist do?

Data annotation specialists are individuals that have expertise and experience in business analytics, data analysis, database management and related fields. They often work in the field of Data Analytics with organizations in many different industries.