Data annotation: Tools, use cases, and future directions [2023] | SuperAnnotate (2023)

When it comes to the global trend nowadays - artificial intelligence and machine learning, the first thing we care about is data. A machine learning model's life starts with data and ends with the deployed model, and turns out that high-quality training data is the backbone of a well-performing model.

Through this article, we'll examine what carries the core responsibility for ready-to-train data, also known as data annotation.

Data annotation is the action of adding meaningful and informative tags to a dataset, making it easier for machine learning algorithms to understand and process the data. Previously, data annotation was not as crucial as it is now for the reason that data scientists were using structured data which did not require many annotations. During the last 5-10 years, data annotation became more critical for machine learning systems so they can work effectively.

Without it, machine learning algorithms would be lost in a sea of unstructured data, struggling to distinguish one piece of information from another. Note that unstructured data makes up a big portion of data in the world – like emails, social media posts, image and audio data, text, sensor data, etc. – thus making the role of data annotation exceptionally important. We can make a bold statement and call data annotation an ingredient in the data processing cycle one can't afford to avoid. With the growing value of AI and machine learning and the exponentially growing amounts of data in the world, data annotation has become even more essential for businesses and organizations to stay competitive.

Whether you are new to data annotation or a seasoned professional, this article will provide valuable insights into the world of data annotation and help you stay on top of its latest trends.

By the end of this article, you'll learn about

  • Image annotation
  • Video annotation
  • Text annotation
  • LiDAR annotation
  • Audio annotation
  • Other annotation types

Data annotation: Tools, use cases, and future directions [2023] | SuperAnnotate (1)

Image annotation

Let's start our data annotation journey with one of the most widely used processes in computer vision, image annotation. Image annotation is the action of tagging digital images with metadata or any additional information that helps to identify and understand the visual content.

Data annotation: Tools, use cases, and future directions [2023] | SuperAnnotate (2)

Breakdown of image annotation and its importance

You can refer to image annotation as the process of making an image easier to find. The best way to achieve this is by giving the image some sort of description, otherwise referred to as annotation. By giving the annotated, structured image dataset to our machine learning models, we allow them to train and deliver the desired results (this depends on the quality of the training data).

With the advent of computer vision and machine learning, image data annotation has become an essential ingredient of data annotation for many applications, including autonomous vehicles, agricultural automation systems, medical imaging, and surveillance systems.

Image annotation tools and use cases

We've touched upon the widespread usage of image data annotation across multiple industries, now it's time to take a closer look at each specific detail and practical application of the tool.

Image classification (or tagging): Image classification is a fundamental data annotation process that involves assigning one or more labels to an entire image. With image classification, you aim to automatically identify the content of an image and categorize it accordingly. Say you're a farmer who wants to analyze crop health. By classifying your training data's crop images, the algorithms can detect early signs of disease or stress, enabling you to take preventative measures and increase your crop yields.

Object detection: Object detection is the action of identifying and localizing objects within an image or a video. It is often confused with image classification, but there is a distinct difference between these two. Image classification refers to categorizing an entire image into one class. Whereas object detection localizes and categorizes objects in an image and assigns tags to each object.

A famous tool for object detection is bounding boxes. Bounding boxes are rectangles that surround an object of interest in an image with the purpose of providing a visual representation of the object's location, like locating pedestrians for autonomous vehicles, identifying people and objects in security camera footage, etc. Its technique is remarkable for its simplicity - it simply doesn't require a complex machine learning algorithm to train.

(Video) Stanford Seminar - Intelligence Augmentation through the Lens of Interactive Data Visualization

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Image captioning (free text description): Image transcription is the process of extracting information from images. It's like making descriptive stories from images and keeping them in the form of textual annotated data. You need to give the tool images and data annotation requirements of the deliverable, and the tool will return the images together with the transcribed information.

Optical character recognition: Optical character recognition (OCR) is a technology that allows computers to read and recognize text from scanned images or documents. This involves, for example, drawing bounding boxes around each line or block of text, which can be used to train OCR algorithms to recognize and extract the text accurately. Let's note that OCR's technology has revolutionized the way we interact with printed and handwritten text. How? It has enabled us to digitalize and preserve historical documents, automate data entry processes, and even enabled accessibility for people with visual impairments. OCR opportunities are diverse and endless, leaving us impatient to witness its future advancement.

Pose estimation (keypoint annotation): Pose estimation is the process of estimating the 2D or 3D coordinates of a human body in a given image or video. It involves detecting and tracking key points on the body and then using the information to determine the position and orientation of the body in 3D space. Key points usually correspond to joints, such as the shoulders, elbows, wrists, hips, knees, or other body parts. Applications of pose estimation in human health are widely recognized to analyze the movement of patients with neurological disorders, such as Parkinson's disease, strokes, and many other cases. It can track a patient's movement and analyze it in real-time, allowing for objective measurements of progress over time.

Data annotation: Tools, use cases, and future directions [2023] | SuperAnnotate (4)

Instance segmentation (polygon annotation): Instance segmentation is a computer vision task that involves identifying and localizing each individual object instance within an image or video and assigning a unique label to each instance (polygon points). Imagine this as a more advanced form of object detection that not only identifies the box coordinates of the objects but also the exact pixel locations of the object.

Polygons is a famous tool for instance segmentation that creates ground truth data. It traces the outline of each object using a set of connected vertices, which define the shape and location of the object. The user typically selects a set of points along the boundary of the object using a polygon tool and then creates the annotated data. It usually takes a lot of time to trace the object which makes it very expensive to create large amounts of polygon datasets. To decrease the annotation time of these tedious tasks, researchers created several AI-assisted algorithms that help users create pixel-precise masks with a few button clicks. SuperAnnotate also integrates state-of-the-art algorithms providing the most accurate and intuitive tools for polygon annotation.

Semantic segmentation: In computer vision, semantic segmentation is referred the AI or ML model which classifies each pixel in the image based on the predefined classes. Semantic annotation is the process of classifying each pixel and is used in many fields such as autonomous driving, retail, and fashion. Classifying each pixel with semantic annotation is probably the most tedious annotation work which makes it really hard for a data scientist to create well-performing semantic segmentation algorithms. To ease the pain of semantic annotation, companies like Segment and SuperAnnotate created the SuperPixel based approach which can accelerate the annotation process by several factors. The SuperPixel-based semantic annotation can be seen below.

Panoptic segmentation: Panoptic Segmentation combines semantic segmentation and instance segmentation into one algorithm. As a result, to annotate an image for panoptic segmentation one needs to use both techniques for semantic annotation and polygon annotation.

Other use cases include rotated box annotation, lane annotation, cuboids, etc. There are many other data annotation practices that are often times used in niche markets. For example, rotated box annotation is quite similar to bounding box annotation, where one needs to specify the rotation angle in the bounding box. Cuboids are also very similar to bounding boxes in their concept, except they capture an object's depth in a 3d format. On the other hand, bounding boxes and lane annotations are very similar to polygon annotations. Such techniques are primarily used in the autonomous driving industry.

Image annotation with SuperAnnotate

SuperAnnotate offers a comprehensive set of tools for accurate and efficient image annotation for all the annotation tasks described above. The platform provides a wide range of user-friendly tools that make it easy to create accurate and precise data annotations. It also offers customization options, allowing users to create their own annotation templates and workflows. With the built-in quality control mechanisms as well as AI-assisted tools, SuperAnnotate makes sure that the annotations are up to their highest standards. SuperAnnotate also provides secure and private data storage, guaranteeing that your data is safe and confidential.

Take a look at this annotation demo video with SuperAnnotate's platform:

Video annotation

Next in the list of commonly used data annotation types is video annotation. To summarize, video annotation is the action of detecting and classifying objects or actions within a video, which is also considered a more complex version of image annotation.

Introduction to video annotation and its importance

Since video data makes up a significant portion of media content, you can already guess the importance of its annotation practice. Let's dive deeper into the world of video annotation.

Video annotation tools and use cases

Video classification (or tagging): Video classification is the process of analyzing and categorizing video content into predefined classes or categories. In internet content moderation, video classification plays an important role in identifying and filtering out inappropriate, offensive, or harmful content, making sure that users have a safe and positive experience.

(Video) Data Annotation Bootcamp

Video captioning (free text description): Similar to image captioning, video captioning deals with extracting story and knowledge from video data and maintains the deliverable in textual form.

Video event or action detection: Video event or action detection is widely implemented in activity recognition and classification in sports videos, drawing a lot of attention from computer vision industry experts. Common applications range from the classification of different actions in sports videos such as a basketball player dribbling the ball or shooting a three-pointer, to performance analysis, athlete recruitment, fan engagement, and much more. Event detection is also widely used as an active learning step in video surveillance applications. In such applications, the events occur rarely, and finding potential annotation frames can be done by event annotation.

Video object detection and tracking: Object detection in videos is the task of identifying the presence of an object in video frame sequences. Object tracking is monitoring an object's movement during a video sequence, including its presence, location, shape, size, etc. Here are some data annotation tools which are efficient for video tracking.

1. Tracking with bounding boxes is a fundamental technique in computer vision that involves detecting, localizing, and tracking objects within a sequence of video frames. Think about how vehicles can be detected and tracked in a traffic video. The process involves drawing bounding boxes around each vehicle and then generating a unique ID that can be used to track the same vehicle in the upcoming frames. Box tracking techniques are widely applied in industries such as autonomous driving, video surveillance, sports analytics, etc.

2. Polygon tracking, also known as video object segmentation, is similar to box tracking annotation but it tracks the exact object boundaries with precise polygons. The annotation is generally much more complex and can take longer if advanced automation tools are not used.

3. Keypoint annotation is used when the object shape itself is not our main concern, but we want to identify critical points within the shape of the object, track those points and know how they move or change their position. This technique is famous in human motion analysis, particularly in sport analytics applications.

Video annotation in SuperAnnotate

There are several factors that one needs to consider when choosing a video annotation tool. For example - for action detection and video captioning, smooth video playback in different speed options is very critical for an efficient annotation. On the contrary, when dealing with object tracking use cases with bounding boxes, polygons, or points supporting frame-based annotation and interpolation is becoming a key to speed up the annotation process. In more advanced use cases, AI-assisted labeling tools based on optical flow, video object tracking, or segmentation are becoming more essential to speed up annotation. SuperAnnotate's video annotation platform is made to speed up the annotation of all these use cases. Additionally, we created several tools to provide efficient collaboration and error detection, making the quality assurance process as important as the annotation process. Here is a small video snippet that demonstrates what the tool looks like.

Text annotation

It's time to learn about the language processing superhero in data annotation. Text annotation is the action of adding extra information to a text with the aim of helping machines understand human language. With text data annotation, machines are able to understand concepts and relationships within texts even if they're in an unclear form or language. Think of it as giving machines magic glasses to see through the complexity of human language.

Data annotation: Tools, use cases, and future directions [2023] | SuperAnnotate (5)

Introduction to text annotation and its importance

Data annotation in text space is becoming more important than ever, especially with new applications created by ChatGPT or other large language models (LLM). However, before LLM use cases became popular, text annotation was still playing an integral role in extracting relevant data from various sources of text. In natural language processing (NLP), text annotation tasks are used for applications such as sentiment analysis, entity recognition, translation, and many more.

Text annotation tools and Use Cases

Text classification: Text classification is one of the most foundational tasks of NLP. Text classification algorithms analyze and recognize patterns within the text and accurately assign the to appropriate categories. These algorithms are instrumental in a wide array of applications, such as sentiment analysis, spam filtering, topic detection, and document organization.

Language translation: The name itself is pretty self-explanatory. Language translation is about using machine learning models to understand text data and translate them to another language. One of the most vivid applications is in communications, where AI translation can facilitate multilingual communication between individuals and groups that speak different languages. This can be particularly useful for businesses that operate globally, or for international conferences and events.

Named entity recognition (entity annotation): Named entity recognition is another text annotation technique that is used for unstructured data annotation. NER involves identifying and annotating data of named entities with specific categories. You can understand entities under the same category as words or phrases that explain similar concepts or mean the same thing. Take the sentence

"SuperAnnotate was ranked as the best data annotation platform in G2".

In this sentence, we can extract multiple entities: SuperAnnotate and G2 fall under "company" category, and "data annotation platform" belongs to a "product" entity. Scale this example and you will have a proper understanding of entity annotation!

Data annotation: Tools, use cases, and future directions [2023] | SuperAnnotate (6)

(Video) 11 Best Marketing Automation Software Tools in 2023 (Ranked by Categories)

Coreference resolution (relationship annotation): Coreference resolution or relationship annotation is a text annotation task that identifies all phrases that refer to the same exact entity. To not confuse it with entity annotation, let's jump straight to a similar example.

"SuperAnnotate was ranked as the best data annotation platform in G2. The company received 92 reviews with 4.9/5 score in the world's largest software marketplace."

In this case, "the company" in the second sentence refers to "SuperAnnotate", and "world's largest software marketplace" refers to "G2". With a text annotation tool, the annotated data will look the following way:

Data annotation: Tools, use cases, and future directions [2023] | SuperAnnotate (7)

Intent annotation: Intent annotation can be considered a subset of text classification but instead of predefined classes, one needs to classify based on the intent of the conversation's response (for example what your customers really want). Intent annotation is the ingredient for understanding the true purpose of text messages. By annotating each message with a specific intent category, such as "Booking Request" or "Complaint", you can unlock powerful insights into your customer's true needs and preferences.

Text annotation with SuperAnnotate

SuperAnnotate's text annotation tools are designed to be intuitive and easy to use, with features like keyboard shortcuts, auto-save, and collaborative annotation capabilities. Users can customize the annotation interface to annotate data based on their specific needs, and adjust settings such as font size, background color, and annotation type.

LiDAR annotation

As we covered image and video annotation, next comes LiDAR, a fancy abbreviation of light detection and ranging. Lidar is a remote sensing technology that uses laser pulses to measure distances between objects. LiDAR annotation has changed the game of data annotation and we're going to show you how.

Data annotation: Tools, use cases, and future directions [2023] | SuperAnnotate (8)

What is LiDAR data and why annotate it?

The annotation techniques that we already discussed mostly covered detecting data in 2D space. However, let's not overlook the fact that we need a tool to calculate 3D information such as depth, the distance between objects, the reflectivity of the objects, and other cases where 2D techniques lack efficiency. LiDAR annotation addresses such issues and we're about to find out how.

To understand the most common application of LiDAR data annotation, let's first learn a new terminology: sensor fusion. Sensor fusion is the process of data collection from multiple sensors to create a more accurate and comprehensive understanding of the environment. In fact, information from just one source tends to be more biased and incomplete compared to combined annotated data from different sources. LiDAR is great for detecting the distance and position of objects in 3D space, but it can't always provide the full picture. That's where images come in, providing additional details such as color and texture.

LiDAR gained its popularity mainly after the recent hype around autonomous vehicles. As self-driving cars become more and more prevalent, LiDAR annotation emerges as a key technology that enables them to safely navigate their surrounding. Let's discuss the case of fusing LiDAR and images to create a more robust and accurate perception of autonomous vehicles.

LiDAR annotated data can provide accurate distance measurements for detecting obstacles and identifying road features. However, LiDAR data annotators alone cannot provide detailed information about the color, texture, and appearance of objects. By combining annotated images with LiDAR data, autonomous vehicles can extract additional information, such as object color and texture to facilitate their understanding of the environment.

LiDAR annotation use cases

LiDAR segmentation: Existing for the past 10 years, LiDAR technology has very recently become a hot topic, especially in LiDAR autonomous driving, due to its ability to deliver detailed 3D information about a vehicle's surroundings. This information includes obstacles, their position, the velocity with respect to the vehicle, and other data which is crucial for a safe driving experience. LiDAR segmentation tries to predict a point and labels it based on predefined labels. Accurate LiDAR segmentation algorithms allow autonomous systems to correctly identify all the obstacles and the road in the street thus making driving safer.

Object detection: Object detection with 3D bounding boxes for LiDAR data is the process of identifying and classifying objects in the point cloud data. Object detection in LiDAR data can be done much easier than segmentation and is often used for detecting pedestrians and cars for autonomous driving companies.

Sometimes the LiDAR data is collected as a sequence of frames. In such cases, Object Tracking becomes an important part of LiDAR annotation. Interpolation, AI-assisted labeling, and automated tracking algorithms are becoming essential in cases when one wants to perform fast and accurate annotation on LiDAR data.

LiDAR annotation with SuperAnnotate: SuperAnnotate provides both LiDAR annotation software and LiDAR annotation services. Choosing a vendor for both your software and services is not an easy task since there is a tradeoff between quality, price, scalability, security, domain expertise, etc. To make things simpler, we summarized several components that you need to consider when choosing a LiDAR annotation partner in a previous article. A visual summary of these components can be found below.

(Video) Generic Annotation Vs Detail Items Family in Revit

Data annotation: Tools, use cases, and future directions [2023] | SuperAnnotate (9)

Audio annotation

Audio annotation has revolutionized the game of sound in the modern world. Every time we ask Shazam to find that sound we really like, ask Siri a question, or Spotify to recommend us a new song, we're using the benefits of audio data annotation. This technology allows us to categorize different types of sounds from voice assistants to wildlife monitoring.

Put on the headphones to crank up the volume and get into the world of "singing" data annotation.

Data annotation: Tools, use cases, and future directions [2023] | SuperAnnotate (10)

Why annotate audio?

Audio data is generated every day, and without its annotation, large amounts of audio value would be lost. With audio annotations, we can train machines to recognize and categorize different types of audio data, from speech and music to ambient noise and animal sounds. With this powerful tool, we can unlock a world of possibilities for everything from speech-to-text transcription to music recommendation systems.

Audio annotation use cases

Audio classification: Audio classification is the data annotation process of classifying sound training data based on their characteristics. The objective of audio classification is to enable machines to identify and differentiate various types of audio, including music, speech, nature sounds, and many more. It's also widely used to classify music genres which helps companies like Spotify and others to recommend similar music based on its genre.

Audio transcription: Audio transcription is the simple process of converting spoken words of audio files into written text. A very useful practice of audio transcription is creating captions for audio and video materials such as interviews, films, or TV shows. Automating audio annotation is very essential for collecting high-quality training data. Whisper is a recent algorithm by OpenAI that helps transcribe audio files in different languages. The transcription is not always accurate when using such automated models, and to correct the initial model's predicted transcription one needs efficient audio annotation transcription tools. Luckily we have a great step-by-step tutorial on how to efficiently clean the prediction of an audio transcription model.

Other types of data annotation

We already discussed the fundamental types of data annotation, but there are a few more that we shouldn't omit due to their widespread use in different industries. Let's explore a few og those data annotation methods.

PDF annotation

A lot of documents are kept in PDF format, making PDF annotation a necessity in financial, legal, and governmental organizations for digitalization purposes. PDF annotation is the action of adding notes, comments, or other metadata to a PDF document to provide additional information or feedback.

Website annotation

Website annotation is the process of adding notes or comments on a live website page, as well as classifying different websites based on predefined classes. It is often needed for content moderation for multiple purposes such as finding out whether the website is safe or not, or whether it contains any nudity, hate speech, etc.

Time series annotation

Time series data annotation involves annotating data that changes over time, such as sensor readings, stock prices, and ECG data. It is often used to predict abnormal activities and anomalies and the annotation tools help to identify and localize those events in the Times series data.

Medical data annotation

Medical data annotation involves annotating various medical images and records, such as X-rays, CT scans, and patient records. With relevant information, it becomes easier to develop accurate machine learning models for medical diagnosis and treatment.

Annotating any other data types with SuperAnnotate

With SuperAnnotate, you can bring your own data format and build a custom annotation editor that is best suited for your annotation needs. Our robust project management and data management toolset will be attached to the annotation editor that you will build and in turn enable the creation of high-quality training data at scale. As a part of the custom editor, we already released an HTML editor, a PDF annotation editor, and a Website annotation editor. You can read more about these editors in our documentation.

Final notes

In conclusion, data annotation plays an essential role in the success of supervised machine learning models as it provides accurately labeled datasets, and serves as the foundation for training data infrastructure. By employing robust annotation techniques for images, video, text, and audio files, machine learning engineers can ensure that their models effectively learn from high-quality annotations. These annotations become the new oil for companies trying to reach AI supremacy. We, at SuperAnnotate, are happy to fuel these companies to advance their AI capabilities through our platform and integrated services.

Data annotation: Tools, use cases, and future directions [2023] | SuperAnnotate (11)

(Video) Top 10 Data Visualization Tools in 2023 | Best Tools for Data Visualization | Edureka


Is there any future in data annotation? ›

According to the research, the global data annotation market is projected to be worth USD 8.22 billion by 2028. Besides, through 2030, it's expected that the global data annotation services market will grow at a CAGR of 26.6%.

What are three tools used in annotation? ›

12 Annotation Tools To Consider
  • Adobe Acrobat Pro DC. Adobe Acrobat Pro DC is a great tool for PDF users that offers an easy, intuitive way to share and manage all of your documents. ...
  • Filestage. ...
  • Annotate. ...
  • ClickUp. ...
  • MarkUp. ...
  • PDF Annotator. ...
  • Drawboard. ...
  • Doccano.
Sep 30, 2022

What are data annotation tools? ›

A data annotation tool is a cloud-based, on-premise, or containerized software solution that can be used to annotate production-grade training data for machine learning.

What are the uses of annotation tool? ›

An annotation tool is a text or drawing tool that helps you add information to text, an image, a database, or any other piece of content. But that's not all. These tools can also be used on various formats such as a whiteboard or PowerPoint presentation, to annotate the selected content.

What is the salary of data annotation specialist in Microsoft? ›

₹4,32,374 - ₹4,69,895

Data Annotation salaries at Microsoft can range from ₹4,32,374-₹4,69,894.

What is the salary of data Annotator? ›

Data Annotator salary in India ranges between ₹ 1.0 Lakhs to ₹ 4.2 Lakhs with an average annual salary of ₹ 2.0 Lakhs.

What are the 5 annotation strategies? ›

How do you annotate?
  • Summarize key points in your own words.
  • Circle key concepts and phrases.
  • Write brief comments and questions in the margins.
  • Use abbreviations and symbols.
  • Highlight/underline.
  • Use comment and highlight features built into pdfs, online/digital textbooks, or other apps and browser add-ons.

What are the 6 types of annotations? ›

Types of Annotations
  • Descriptive.
  • Evaluative.
  • Informative.
  • Combination.
Aug 12, 2022

What is the best annotation tool? ›

What Are The Best Annotation Tools?
  • Adobe Acrobat Pro DC.
  • Markup Hero.
  • Annotate.
  • Filestage.
  • zipBoard.
  • ClickUp.
  • PDF Annotator.
  • Hive.
Dec 15, 2022

What are the different types of data annotation? ›

Infographic – 4 Types of Data Annotation

It can range from image and video annotation, text categorization, semantic annotation, and content categorization.

What skills are needed for data annotation? ›

Your ability to focus on the details, identify objects, and correctly label the data, each and every time, will make you successful. Accuracy is critical when it comes to data annotation, and your ability to focus on details can help you stand out.

Why is @data annotation used? ›

Data annotation is the process of labeling data with relevant tags to make it easier for computers to understand and interpret. This data can be in the form of images, text, audio, or video, and data annotators need to label it as accurately as possible.

What are the four benefits of annotation? ›

The benefits of annotation include:
  • Keeping track of key ideas and questions.
  • Helping formulate thoughts and questions for deeper understanding.
  • Fostering analyzing and interpreting texts.
  • Encouraging the reader to make inferences and draw conclusions about the text.

Which is an example of the use of annotations? ›

For example, a note that you scribble in the margin of your textbook is an annotation, as is an explanatory comment that you add to a list of tasks at work. Something that has had such notes added to it can be described as annotated. The word annotation is sometimes abbreviated as annot.

What is the goal of annotation? ›

Why Annotate? By annotating a text, you will ensure that you understand what is happening in a text after you've read it. As you annotate, you should note the author's main points, shifts in the message or perspective of the text, key areas of focus, and your own thoughts as you read.

Is data annotator a good job? ›

Machine Learning Engineer: Data annotation provides valuable experience working with machine learning models and algorithms. This can be an excellent foundation for a career as a machine learning engineer, who is responsible for developing and deploying machine learning models and algorithms.

What is the career growth as data annotator? ›

The job demands long periods of focus, attention to detail, and ability to handle different aspects of the machine training process. The freshers in the field of data annotation can expect packages ranging from INR 1.1 lakhs to INR 3 lakhs per annum.

How much does an annotator get paid per hour? ›

$16.35 is the 25th percentile. Wages below this are outliers. $32.21 is the 75th percentile.

Is data annotation easy? ›

The more annotated data you use to train the model, the smarter it becomes. But you can't do it without a little help. Producing the necessary annotation from any asset at scale is a challenge for many – mainly due to the complexity involved with annotation. Getting the most accurate labels demands time and expertise.

What is the highest paid data salary? ›

Highest salary that a Data Analyst can earn is ₹11.6 Lakhs per year (₹96.7k per month).

What is the highest salary in data field? ›

Here are high paying data science positions with average salaries over $65,000 per year:
  • Business intelligence developer. ...
  • Infrastructure engineer. ...
  • Data scientist. ...
  • Data modeler. ...
  • Big data engineer. ...
  • Data architect. ...
  • Enterprise architect. ...
  • Machine learning engineer. National average salary: $149,847 per year.
Jan 17, 2023

What are the 4 layers of annotation? ›

The word layer is also called the w-layer, the morphological one the m-layer, the analytical one the a-layer, and the tectogrammatical one the t-layer.

What are the 3 most common types of annotations? ›

  • HIGHLIGHTING/UNDERLINING. Highlighting or underlining key words and phrases or major ideas is the most common form of annotating texts. ...

What are the 4 components of annotation? ›

Write an Annotated Bibliography
  • Citation information.
  • Purpose.
  • Content.
  • Relevance.
Oct 27, 2022

What are the keys for annotation? ›

Keyboard shortcuts for annotation order
Keyboard shortcutFunction
Ctrl + [Move an annotation up the Annotations list
Ctrl + ]Move an annotation down the Annotations list
HHide all annotations
Shift + HHide all sub-annotations

What are three examples of an annotation? ›

An annotation might look like highlighting information information or vocabulary in a text, marking a text with symbols to represent different ideas, creating notes in the margins of a text to keep track of thoughts and questions, or writing summaries at the end of a chapter or section for easy review.

What is a digital annotation tool? ›

Digital annotation is the process of adding text or handwriting to existing digital files (e.g. uploaded student PDFs assignments or. journal articles).

What is smart annotation? ›

Smart Annotation is an AI-powered Autodesk® Revit® add-in to automate annotating and documentation process of the drawings. The AI algorithm behind the app places tags in their preferred positions and aligns them with other tags without overlapping with other elements in the document.

What are good annotation starters? ›

Annotation Sentence Starters

This ​book​ helped me / us to understand… This ​document​ was important to my/our topic because… After reading this ​newspaper article​ I / we …. I / We used this ​speech​ to…

What is data annotation for beginners? ›

Text Data Annotation Tutorial
  1. Step 1: Create a New Project. ...
  2. Step 2: Import the Data Set. ...
  3. Step 3: Choose the Data Annotation Type and Template. ...
  4. Step 4: Configure the Named Entity Recognition Parameters. ...
  5. Step 5: Label the Data into Animal and Object Categories.
Sep 14, 2022

What is data annotation projects? ›

Data annotation is the process of labeling individual elements of training data (whether text, images, audio, or video) to help machines understand what exactly is in it and what is important. This annotated data is then used for model training.

What is the job role of data annotator? ›

Job Description: The Data Annotator / Data Labeller will support the Research and Development Team in improving the accuracy and quality of our deep learning models to achieve excellent performance in projects and competitions by using in-house tools to accurately annotate and label data.

How do I become a data annotator? ›

For the data annotator job you don't need to be an expert in computers but you should have knowledge of how to work on different platforms. Familiarizing yourself with the tools and techniques used in data annotation, such as text annotation tools, image annotation tools, and audio/video annotation tools.

How much does a data annotation specialist make at Tesla? ›

The estimated total pay for a Data Annotation Specialist at Tesla is $28 per hour.

What is annotation in Selenium? ›

Annotation is repeatedly used by developers to describe a piece of code that is inserted into the program or business logic used to control the flow of methods in the programming language Java. Annotations play a major role in Test Next Generation (TestNG), an automation framework widely used by Selenium.

What is annotation in Python? ›

Annotations were introduced in Python 3.0 originally without any specific purpose. They were simply a way to associate arbitrary expressions to function arguments and return values. Years later, PEP 484 defined how to add type hints to your Python code, based off work that Jukka Lehtosalo had done on his Ph.

What is annotation strategy? ›

Annotations are a critical strategy teachers can use to encourage students to interact with a text. They promote a deeper understanding of passages and encourage students to read with a purpose.

Is data annotation a good career? ›

The job demands long periods of focus, attention to detail, and ability to handle different aspects of the machine training process. The freshers in the field of data annotation can expect packages ranging from INR 1.1 lakhs to INR 3 lakhs per annum.

Is data Annotator a good job? ›

Machine Learning Engineer: Data annotation provides valuable experience working with machine learning models and algorithms. This can be an excellent foundation for a career as a machine learning engineer, who is responsible for developing and deploying machine learning models and algorithms.

Will data analytics continue to grow? ›

Data analytics market to grow by USD 21,436 million between 2022 and 2027; Growth driven by extensive use of modern technology in company operations - Technavio.

What is the salary of data annotation in Apple? ›

How much does a Annotation Analyst at Apple make? The typical Apple Annotation Analyst salary is ₹7,14,648 per year. Annotation Analyst salaries at Apple can range from ₹4,98,391 - ₹10,00,000 per year.

How much does an Annotator get paid per hour? ›

$16.35 is the 25th percentile. Wages below this are outliers. $32.21 is the 75th percentile.

What is the salary of annotation lead? ›

The national average salary for a Data Annotation Manager is $50,938 per year in United States. Filter by location to see a Data Annotation Manager salaries in your area. Salaries estimates are based on 1 salaries submitted anonymously to Glassdoor by a Data Annotation Manager employees.

What skills does a data Annotator need? ›

Role Specific Skills
  • Active Learning.
  • Active Listening.
  • Category Flexibility.
  • Complex Problem Solving.
  • Computers and Electronics.
  • Coordination.
  • Critical Thinking.
  • Deductive Reasoning.

How much does Apple pay annotation quality analyst? ›

The estimated total pay for a AI/ML Annotation Analyst at Apple is $67,558 per year.

What is the salary of annotation specialist in Apple? ›

Annotation Specialist salaries at Apple can range from ₹5,09,935-₹5,51,940.

What is the highest paying job in Tesla? ›

What is the highest-paid job at Tesla? The highest-paid job at Tesla is a senior software engineer. These professionals earn an average salary of $135,045, which works out to about $ 64.93 per hour.

What are the big data trends in 2023? ›

Advanced analytics and AI/ML continue to be hot data trends in 2023. According to a recent IDC study, “executives openly articulate the need for their organizations to be more data-driven, to be 'data companies,' and to increase their enterprise intelligence.”

Is data analytics a good career in 2023? ›

The world is witnessing a surge in demand for data analytics services. According to a report, it is expected that there will be 250,000 new openings in the Data Analytics field in 2023, which is almost 60% higher than the demand in 2019-20.

What is the data trend in 2025? ›

The world today generates over 64 zettabytes of data in a year — that's 64 trillion gigabytes of data, from a total of 23.8 billion connected devices. By 2025, the world's data will exceed 180 zettabytes, from over 41 billion connected devices. Volume of data/information created, captured, copied and consumed by 2025.


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