Suppose someone has a substantial collection of raw data collection, and now wants to feed that information into artificial intelligence (AI) machines to get human-like actions. But the problem is these machines can only perform according to the parameters established for the data set. Data annotation is the main solution that ties the gap between sample data and AI/machine learning. Data annotation is a method where a human data annotator goes into a raw data set and adds categories, labels, and other contextual elements, so machines can read and act upon the information. With the use of data annotation, the annotated raw data used in AI and machine learning not only consists of numerical data and alphabetical text but can also be applied to images and audio-visual elements.

The algorithm is a step-by-step procedure, which defines a set of directions to be executed in a certain order to get the desired output from the specified raw data. Today’s daily life is guided by algorithms. We count on these algorithms for several reasons, including personalization and efficiency. But their ability to deliver the most satisfactory output by AI is dependent on data annotation, the process of most accurately labeling raw data to train artificial intelligence to make future decisions. Data annotation is the hard work behind our present algorithm-driven world.

Data is the pillar of the customer experience of any kind. Even the simplest decisions — an estimated time of arrival from a GPS app or the next song in the streaming queue — can filter through artificial intelligence (AI) and machine learning algorithms (ML). As brands gather more and more insight into their customer’s behavior, AI can help make the data collected actionable. According to Gartner, by 2022, 70% of customer interactions are expected to filter through technologies like machine learning (ML) applications, chatbots and mobile messaging.

Data annotation has been classified into the following categories, as so many different industries and workspaces work with different data types. Let’s understand a closer look at each annotation type in assisting with data categorization.


Text annotation is the most commonly used data annotation type. Text annotation is essentially the process of using metadata labels to highpoint keywords, phrases, or sentences to teach AI machines how to properly identify and understand human emotions through words. Precision means everything in text annotation. Inaccurate annotation could lead to misinterpretation and make it more complex to understand words in a specific setting. For error-free text annotation machines need to understand all potential phrasing of a certain question or statement based on how humans express or communicate via the internet. When a consumer phrases a question in a different way machine may not be familiar with, it can be difficult for the machine to reach the endpoint and offer a solution. The better the text annotation involved the healthier-customer experience, also helps an organization meet its goals and utilize human resources to the best of its ability. Text annotations include a wide range of annotations like emotion, intent, and interrogation.

USE:  Text Annotating activity interacts with a text to enhance the reader’s understanding and reaction to the text and helps to make sentences meaningful. 70% of companies surveyed in the machine learning report acknowledged the use of text annotation in their system. Various search engines like Google, Microsoft’s Bing, and e-commerce companies like Amazon, and Myntra use text annotation to a large extent.

Sentiment analysis evaluation is based on attitudes, emotions, and opinions, to provide helpful insight that could potentially drive serious business decisions. Human annotators are often leveraged on all web platforms to evaluate sentiment and moderate content. From social media and e-commerce sites to tagging and reporting on keywords can be especially valuable in analyzing sentiment data.

  1. Intent Annotation

Today’s machines must be able to understand both natural language and user intent. Characteristically, when the intent is not recognized by a machine, it will not be able to continue with the request and probably will ask for the information to be rephrased. If the rephrasing of the question is still not alleged, it may hand the question over to a human agent, thus taking away the whole purpose of employing a machine in the first place. Intent Annotation is a multi-intent data collection and categorization that can distinguish intent into various key categories including command, request, booking order, confirmation, and recommendation. These categories make it easier for machines to understand the basic intent behind a query and find a resolution as per the query.

The tagging of specific documents to the conceptualization of the most relevant information is done under Semantic Annotation. This involves adding metadata to documents to enrich the content with concepts and descriptive words to provide greater depth and meaning to the text. Semantic annotation improves product listings so that customers can find the products they’re looking for. This type of annotation turns browsers into buyers.

Named Entity Annotation is used to identify certain articles within the typed text to perceive critical information. Information such as formal names, places, brand names, and other identifiers are examples of what this annotation works for. Organizations like Sunix AI apply named entity annotation technology across a wide range of use cases, such as helping eCommerce clients identify and tag a range of key descriptors, or aiding social media companies in tagging units such as community, places, companies, organizations, and group to assist with better-targeted advertising content.


Audio annotation is a type of data annotation that involves identifying the components in audio data. The audio annotation also includes the transcription of specific pronunciation and intonation, as well as the identification of language and speaker demography. For example, the tagging of hate speech indicators and non-speech sounds like glass breaking for use in security and can be useful in emergencies. Like other types of annotation, audio annotation requires manual labeling and specialized software.

USE: Audio annotation can be applied for a variety of purposes, such as organizing audio files, improving search capability, and making it easier to find specific parts of an audio recording.


Image annotation can be identified as one of the most vital responsibilities of machines in the digital age, as it has allowed interpreting the world through a pictorial lens. Image annotation is the process of labeling images to train an AI or ML model. Image annotation is extremely important for a wide range of areas like computer vision, robotic vision, facial recognition, and solutions that rely on machine learning to interpret images. Metadata tags are assigned to the images in the form of identifiers, captions, or keywords. Depending on the situation, the number of labels on the image may increase. There are four important types of image annotation:

1. Image classification

The machine interacts with annotated images first and then determines what an image denotes with the predefined annotated images.

2. Object recognition/detection

Object recognition/detection is an additional version of image classification. It is the correct explanation of the numbers and exact positions of entities in the image. While a label is assigned to the entire image in the object recognition system. For example, with image classification, the image is categorized as day or night. Object recognition individually tags various objects in an image, such as a bicycle, tree, or table.

3. Segmentation

Segmentation is a more innovative form of image annotation. To evaluate the image more efficiently and effectively, it divides the image into multiple segments, and these parts are called image objects. Image segmentation is of 3 different kinds:

  • Semantic segmentation-Tag similar entities in the image according to their properties, such as their size and location.
  • Instance segmentation-Each object in the image can be marked. It describes the properties of entities such as position and number.
  • Panoptic segmentation- Both semantic and instance segmentations are applied by combining.

USE: From computer vision systems used by self-driving vehicles and machines that pick and sort produce, to healthcare applications that auto-identify medical conditions, are some fields that require high volumes of annotated images. Image annotation increases accuracy and exactness effectively in the system.


Video annotationis the method of teaching computers to recognize objects from videos. Both Image and audio annotation methods are performed to train computer vision (CV) systems, which increases the technical ability of a machine’s artificial intelligence (AI).

USE: Video annotation is widely used in retail store surveillance systems.

Manually annotated data is the key to effective machine learning. Humans are merely better than computers at handling subjectivity, understanding intent, and coping with ambiguity. For example, while determining the relevancy and effectiveness of a search engine result, input from many people is required for consent. When exercising a computer vision or design recognition solution, humans are needed to identify and annotate specific data, such as outlining all the pixels containing building or traffic signs in an image.

As per Grand View Research, the data annotation market in the world is projected to be worth USD 8.22 billion by 2028. The implementation of AI-based services in different sectors has contributed to this increase in demand. Many sectors such as healthcare, automobiles, telecom, and e-commerce are major sectors that expedient to collect datasets from different sources and label them based on their setting, requirements, and features. The integration of digital image processing and mobile computing platforms is one of the key aspects contributing to the growth of this sector. For instance, digital commerce companies are finding this technology vital when it comes to improving user experience and identifying opportunities. Banking, finance, and insurance companies are approving this technology for accurate and efficient document verification and also to interact with customers in real-time. Even in research works, data annotation technology helps to raise scores of accumulated and raw datasets. Social media companies are using it to observe the trend and fads in content. They are also using it to shed off inappropriate content or to influence customers. This tech is increasingly becoming popular in the agriculture sector for crop monitoring, soil assessment, etc.

Despite exponential growth Data Annotation faces some sought of challenges:

  • Cost of annotating data: Data annotation can be done either humanly or mechanically. However, manually annotating data requires a lot of effort, and you also need to maintain the quality of the data, thus associate with a huge cost.
  • Accuracy of annotation: Human faults can lead to poor data quality, and these have a direct impact on the prediction of AI/ML models. Gartner’s study highlights that poor data quality costs companies 15% of their revenue.

Today’s data annotation companies are providing training data for AI and ML models, and also offer deployment and maintenance services for AI and ML projects. Thus, along with the main service they provide follow-up services meant to ensure that the provided data leads to the desired results wherever the ML algorithm is trained using these deployed data. Data annotation companies provide the primary solution that bridges the gap between sample data and AI/machine learning. Data annotation companies work is a continuous process where a human data annotator goes into a raw data set and adds categories, labels, and other contextual elements, so machines can read and act upon the information most effectively and efficiently.

Data annotation company that works on Data annotation platforms use a complex combination of AI and machine learning to process and label data in large numbers. Artificial intelligence identifies the main features in a string of data labeled by a human and then identifies the similarities in other strings. They are given a baseline from which the work can be started, and then incorporated the larger datasets in the system. For accuracy in data annotation platforms, machine learning needs to be highly functional otherwise a lot of room for error. Data annotation companies, by drilling method, help the machine to identify patterns and learn from its mistakes. Along with Data Annotation Company, businesses can be more confident that their data annotation platform is labeling data accurately. Once data has been labeled and sorted, businesses have more ease of access to find what they are looking for. In simple words, the data annotation company properly sorted data, which will lead to faster recall from another machine.

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