Data Annotation Services

Good Data = Awesome AI

Bad Data = Awful AI

Good Data = Awesome AI

Bad Data = Awful AI

Feed Properly Annotated Data To Your ML Models With Our Data Labeling Services

A Machine Learning model is like a baby. If you teach wrong things, it will learn to interpret things wrongly. An improperly annotated dataset can make your ML model interpret Huskies as wolves! And this is where data labeling companies come in. We get it; in the exciting world of Artificial Intelligence, labeling the data in the datasets is perhaps the most boring, unsexy work. Yet, at the same time, no ML model in the world can work as intended if it is fed with improperly labeled data.

Data Labeling

Our Data Labeling Company in India Understands the Importance of Properly Annotated Data

Challenges That Our Data Annotation Service Addresses

Our data annotation services address all the pre-processing problems pertaining to data labeling. Since data is the backbone of any neural network, it is essential that you address these challenges to make the ML model work as intended.

Challenge 1: Lack of Objectivity
The biggest challenge when it comes to data labeling is the fact that data labeling HAS to be done by humans. And the way one human being thinks differs from the way another one thinks. As a result, one image can have different interpretations. Or perhaps, when you are teaching your neural network the sentiment behind a particular sentence, one human data labeler might find the piece of text offensive, while another human data annotator might not have any problem with the same piece of text. When subjectivity creeps into the dataset, its integrity becomes questionable.
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Challenge 2: Lack of Scalability
Yes, there are many data annotation tools. However, labeling data is mostly a manual task. And scaling a manual task is not that easy. At the end of the day, how many manual data labelers are working for you will define how fast you can annotate data.
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Challenge 3: Lack of Domain Experts
Data Annotation might look like a generic, low-skill job. In reality, it requires the help of domain experts. For example, if you are teaching a virtual AI-powered robot to identify and mimic various dance moves, you need to feed it videos of various dances. Now, these videos need to be properly annotated. And this can only be done properly by a professional dancer - not by others who can’t tell one dance from the other.
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Challenge 4: Protecting Privacy
When a company tries to leverage its data to build an ML model, the first roadblock that comes in front of it is the challenge of protecting the sensitive information that the data contains. This is exactly why it can’t easily assign the data annotation work to any offshore freelancer.
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Our Solution To These Data Annotation Challenges

Our data labeling company successfully addresses all these challenges. 

Our Data Annotation Services

Image Annotation Services

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Image Annotation

We annotate, tag and provide titles or descriptions to images that are intended to be fed to the ML system. We make no assumptions while annotating images, and we label the images based on what they display.

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Sentiment Evaluation Service

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Sentiment Evaluation

As part of our NLP data labeling services, we specify what kind of emotion is being displayed in a given sentence. Since a sentence can have a deeper meaning than what is implied on a surface level, we assign language experts for this job.

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Audio Classification Services

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Audio Classification

From transcribing audio to classifying audio snippets based on the requirement - we provide a host of audio classification services. We have writers who specialize in English, Hindi and Bengali audio samples.

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Semantic Segmentation Service

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Semantic Segmantation

The word - ‘Glasses’ can mean either spectacles or glassware. How can an ML model understand the difference? This is where semantic segmentation comes in. We label specific pixels of specific elements shown in the image in order to teach the model how to differentiate between various images and the concept they are associated with.

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Video Data Annotation Services

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Video Data Annotation

We provide services like key-point annotation to label objects shown in videos. As part of key-point annotation, we further provide data annotation services for AI that are used for facial expression recognition, human body part recognition etc.

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Object Classification With Bounding Box

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Object Classification

Sometimes an image can have too many similar elements in it. For example, how do you teach an ML model to differentiate between a cat and a small dog? This is where bounding box-based object classification comes in. We offer such object classification for computer vision and other projects.

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Polygon Annotation Services

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Polygon Annotation

We have image label annotators who are skillful in photoshop. These annotators use polygon annotation to label images of real-world objects that are not entirely rectangular or entirely circular. For example, we use polygon annotation to specify the shape of vehicles, household objects, animals, and humans - all of them have non-rectangular shapes.

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Handwriting Recognition Services

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Handwriting Recognition

Our data annotation service also includes handwriting recognition. Our English content writers specialize in identifying words and sentences written by hand (or finger, in case the text is written on a touch screen).

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Our Story

Why – as a Digital Marketing Agency – We Support Artificial Intelligence? Won’t They Take Away Our Job?

According to the author of the book – The Innovator’s Dilemma – when a company doesn’t embrace disruptive technology, it soon becomes redundant. For example, Blockbuster – the offline video rental company – failed to adapt to the changing world. Netflix and its online-first approach toppled it from the leading position.

We don’t want this to happen to us. That is why, despite being a content marketing agency, we support Artificial Intelligence.

Our data labeling service is our way of embracing disruptive technology before it gets too late.

Contact Us To Train Your ML Model With Accurately Labeled Data