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Machine Learning and Artificial Intelligence are rapidly growing technologies giving rise to unbelievable inventions delivering advantages to several fields globally. And to develop such automated machines or applications, an enormous amount of training data sets is expected.

What is Data Annotation?

Data annotation is the procedure of labeling the data accessible in various layouts like video, text, or images. For supervised appliance learning, labeled data sets are expected so that machines can clearly and easily comprehend the input patterns.

And to equip computer vision with an established machine learning model, it needs to be precisely annotated using adequate tools and methods. And numerous types of data annotation methods are used to develop such data sets for such necessities.

Why is Data Annotation Required?

We understand for a fact that computers are competent at providing ultimate outcomes that are not just exact but related and timely as well. Nonetheless, how does an appliance learn to provide such efficiency?

All thanks to data annotation. When machine learning is nonetheless under improvement, they are provided with volumes after volumes of Artificial Intelligence training data to prepare them better at making judgments and identifying elements or objects.

Only through data annotation could modules distinguish between a dog and a cat, an adjective and a noun, or a sidewalk from a road. Without data annotation, every impression would be the exact same for machines as they do not have any ingrained information or understanding about anything on the planet.

Data annotation is expected to make networks deliver detailed results, help modules specify elements to equip computer speech and vision, and recognize models. For any system or model that has a machine-driven decision-making system at the fulcrum, data annotation is expected to assure the decisions are relevant and accurate.

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Data Annotation Use Cases

Data annotation is beneficial in:

Enhancing the Quality of Search Engine Outcomes for Multiple Users

Search engines require users to provide detailed information. Their algorithms must filter high quantities of labeled datasets to give an adequate answer to do. For instance, Microsoft’s Bing. Back it caters to numerous markets; the vendor must ensure that the outcomes the search engine would deliver would match the user’s line of business, culture, and so on.

Improving Local Search Evaluation

While search engines seek a global audience, dealers also have to ensure that they give users localized outcomes. Data annotators can enable that by labeling images, information, and other subjects according to geolocation.

Improving Social Media Content Relevance

Just as search engines, social media outlets also need to deliver customized content suggestions to users. Data annotation can enable developers to categorize and classify content for pertinence. An instance would be classifying which content a user is inclined to consume or understand based on his or her viewing patterns and which he or she would find relevant based on where he or she resides or works.

Data annotation is tedious and time-consuming. Thankfully, AI (artificial intelligence) systems are now accessible to automate the procedure.

What is a data Annotation tool?

In simple phrases, it is an outlet or a portal that lets experts and specialists annotate, label, or tag datasets of all categories. It is a medium or a bridge between raw data and the outcomes your machine learning modules would eventually churn out.

Data labeling equipment is a cloud-based or on-prem solution that annotates excellent quality training data for machine learning. While many firms rely on an outer vendor to do complicated annotations, some institutions still have their own equipment that is either custom-built or established on freeware or open-source devices accessible in the market. Such devices are usually constructed to handle particular data types i.e., video, image, text, audio, etc. The devices offer options or features like bounding polygons or boxes for data annotators to label pictures. They can just choose the option and execute their particular tasks.

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What are the Advantages of Data Annotation?

Data annotation is immediately aiding the machine learning algorithm to get equipped with supervised learning procedures accurately for good prediction. Nonetheless, there are a few benefits you need to understand so that we can comprehend its significance in the AI world.

Enhances the Accuracy of Output

As much as picture annotated data is utilized for training the machine learning, the precision will be higher. The diversity of data sets used to equip the machine learning algorithm will understand different types of characteristics that will help the model to operate its database to give adequate results in numerous scenarios.

More Enhanced Knowledge for End-users

Machine learning-based equipped AI models to deliver wholly different and seamless knowledge for end-users. Virtual assistant equipment or chatbots assist the users instantly as per their necessities to solve their questions.

Furthermore, in web search engines such as Google, the machine learning technology provides the most related outcomes using the examination relevance technology to enhance the outcome quality as per the past searching manner of the end-users.

Similarly, in speech recognition technology, virtual assistance is used with the benefit of natural language processes to comprehend human terminology and communication.

Text annotation and NLP annotation are part of data annotation, developing the training data sets to formulate such models delivering more enhanced and user-friendly understanding to various people globally through numerous devices.

Analytics is delivering full-fledged data annotation assistance for AI and machine learning. It is implicated in video, text, and image annotation using all categories of techniques per the consumers' provision. Working with competent annotators to deliver a reasonable quality of training data sets at the lowest cost to AI customers.

Why does data annotation matter?

Data annotation is the procedure of labeling data in several formats like images, video, or text so that appliances can comprehend it. For supervised labeled databases, machine learning is essential because ML models require understanding input patterns to process them and generate detailed results. Supervised ML models process and understand from correctly annotated data and interpret difficulties such as:

  • Classification: Assigning test data into particular classifications. For example, indicating whether a patient has a disorder and selecting their health data to “no disease” or "disease" sectors is a classification problem.
  • Regression: Ascertaining a relationship between independent and dependent variables. Totaling the relationship between the budget to publicity and sales of a commodity is an instance of regression difficulty.

For instance, training machine learning to drive cars involves annotated video data. Particular items in videos are annotated, enabling appliances to indicate objects' movements.

Data annotation is called data tagging, labeling, classification, or machine learning. Annotated data is known to be the lifeblood of supervised learning models since such models' accomplishment and precision rely on the quality of annotated data. Annotated data matters:

Machine learning categories have a broad variety of significant applications. Finding excellent quality annotated data is one of the significant challenges of building machine learning. Data is an essential part of the customer experience. How nicely you know your customers directly impacts the quality of their understanding. As brands collect more and more information on their consumers, AI can make the data compiled actionable.

“AI interactions will improve voice, text, sentiment, interaction, and even conventional survey analysis," says Gartner's vice-president on the analyst corporation's blog. But in order for virtual and chatbot assistants to develop seamless customer experiences, brands require to make specific the datasets guiding these judgments are high-quality.

As it presently stands, data scientists use a substantial portion of their time preparing data, per the survey by data science outlet Anaconda. Part of that is consumed by fixing or removing anomalous or non-standard articles of data and making sure distributions are valid. These are essential tasks, provided that algorithms depend heavily on understanding structures in order to make judgments and that faulty data can be interpreted into biases and bad predictions by AI.

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What is the disparity between data labeling and data annotation?

They imply the same thing. You will come off articles that attempt to explain them in several ways and compose discrepancies. Terminology is not an excellent medium; people can imply different aspects even when they utilize the exact phrases. Nonetheless, based on our conversations with dealers in this area and with data annotation users, there is no discrepancy between these notions.

What are the fundamental challenges of data annotation?

The expense of annotating data: Data annotation can be done automatically or manually. Nonetheless, manually annotating data compels a lot of effort, and you must also maintain the data's integrity.

Accuracy of annotation: Human omissions can lead to bad data quality and immediately impact the projection of AI/ML models. Gartner’s research highlights that bad data quality costs corporations fifteen percent of their revenue.

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Types of Data Annotation

Creating an AI or ML model that works like a human needs large quantities of training data. For a model to create decisions and seize action, it must be equipped to comprehend specific data. Data annotation is the categorization of data for Artificial Intelligence applications. Training data must be appropriately annotated and categorized for a particular use case. Firms can create and enhance AI implementations with excellent quality, human-powered data annotation. The outcome is an enhanced customer knowledge solution like product recommendations, related search engine outcomes, speech recognition, computer vision, chatbots, and more. There are various primary types of data: audio, text, image, and video.

Text Annotation

The most generally used data category is the text as per the 2020 State of AI and Machine Learning report, seventy percent of companies depend on the text. Text annotations comprise a broad range of annotations like intent, sentiment, and query.

Sentiment Annotation

Sentiment analysis examines emotions, attitudes, and opinions, making it significant to have accurate training data. To retain that data, human annotators are frequently leveraged as they can assess sentiment and appropriate content on all web outlets, comprising social media and eCommerce areas, with the capacity to tag and report on sensitive, profane tags, or neologistic, for instance.

Intent Annotation

As you converse with human-machine interfaces, devices must be eligible to comprehend both user intent and natural language. Multi-intent data categorization and collection can distinguish intent into key classifications: command, request, booking, confirmation, and recommendation.

Semantic Annotation

Semantic annotation enhances product listings and assures customers to discover the products they are looking for. This enables them to turn browsers into buyers. By indexing the various elements within product search queries and titles, semantic annotation services aid in training your algorithm to comprehend those individual parts and enhance overall search applicability.

Named Entity Annotation

NER (Named Entity Recognition) systems need a large quantity of manually annotated training. Institutions like Appen pertain named entity annotation capabilities across a broad range of use cases, such as enabling eCommerce clients to specify and tag a span of key descriptors or benefiting social media corporations in tagging entities like places, people, titles, companies, and organizations to aid with better-targeted publicity content.

Audio Annotation

Audio annotation is the time-stamping and transcription of speech data, comprising the transcription of certain information and pronunciation and the identification of dialect, language, and speaker demographics. Each use case is unique, and some need a very particular approach: for instance, the tagging of forceful speech indicators and non-speech tones like glass breaking for practice in emergency and security hotline technology applications.

Image Annotation

Image annotation is essential for many applications, including robotic vision, computer vision, facial recognition, and solutions that bank on machine learning to infer images. To train these explanations, metadata must be appointed to the images in the structure of captions, identifiers, or keywords. From computer vision networks used by self-driving automobiles and machines that grab and sort produce to healthcare applications that identify medical situations, several use cases need high volumes of annotated pictures. Image annotation boosts accuracy and precision by effectively equipping these systems.

Video Annotation

Human-annotated data is fundamental to profitable machine learning. Humans are clearly better than computers at understanding intent, managing subjectivity, and coping with vagueness. For instance, when inferring whether a search engine finding is relevant, intake from many people is required for agreement. When acquainted with a computer pattern or vision recognition solution, humans must specify and annotate particular data, such as summarizing all the pixels, including trees or traffic signs in a picture. Machines can utilize this structured data to recognize these connections in testing and output.

Key Steps in Data Annotation Procedure

Occasionally it can be helpful to talk about the stage processes that come in complicated data annotation and labeling projects.

  • The first phase is acquisition. Here is where corporations compile and aggregate data. This phase generally involves having to base the subject matter aptitude on human operators or through a data licensing agreement.
  • The procedure's second and prominent step involves annotation and labeling. This step is where the NER and intent examination would take place. These are the essentials of accurately indexing and labeling data to be used in machine learning programs that succeed in their objectives and goals.
  • After the data have been adequately indexed, labeled or annotated, the data is mailed to the third and ultimate stage of the procedure: deployment or output. One thing to remember in mind about the application stage is the requirement for compliance. This is the phase where privacy problems could become complicated. Whether it’s GDPR or HIPAA or other local or federal approaches, the data in play may be data that is sensitive and must be regulated. With awareness of all of these components, that three-step procedure can be uniquely beneficial in developing outcomes for industry stakeholders.

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In a similar way that data is continually evolving, the data annotation procedure is becoming more sophisticated. To put it in perspective, 4-5 years ago, it was sufficient to label a few notches on a face and build an AI prototype based on that data. Now, there can be as many as twenty dots on the lips alone.

The continuous transition from scripted chatbots to AI is one of the promising to bridge the rift between natural and artificial interactions. At this time, consumer confidence in AI-derived solutions is deliberately increasing. A study found that people were more inclined to ratify an algorithm's suggestions when they arrived at a product's practicality or accurate performance.

Algorithms will proceed to shape consumer understanding for the foreseeable fate — but algorithms can be flawed and can endure the same prejudices of their creators. Assuring AI-powered experiences are fascinating, efficient, and beneficial needs data annotation done by various teams with a fine understanding of what they are annotating. Only then can one assure data-based solutions are as detailed and representative as feasible.

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