Data Annotation


Annotation is the process of labeling data to make it understandable for machine learning and it’s utmost necessary to have accurate sets for Machine Learning.  Data annotation, an important step of data preprocessing in supervised learning. Machine Learning (ML) dictates a new approach to business – one that requires plenty of data. 


Bounding boxes are imaginary boxes drawn on an image, shape, or text and then we label the contents of the box to help a model recognize it as a distinct type of object. Bounding boxes are one of the most common ways that image or video annotation is done.


Chatbots recognize words and phrases in order to deliver helpful information to customers who have some common questions. Sometimes, chatbots are so accurate that it seems as if you’re talking to a real person. For many industries like education, medical transport, and e-commerce or the enterprise, retaining or understanding their users requires efficient and effective customer support. There are many companies that make use of AI & machine learning to improve the customer support experience. One of the most important processes in Chatbots is Data Annotation assigned to Data labeling companies to get accurate results and achieve goals 

Intent Analysis


Deep learning is a subset of machine learning (ML) which is a sub-discipline of artificial intelligence (AI). Deep learning is used to carry out more crucial tasks without being explicitly programmed to do so. Basically, in deep learning neural networks are used to analyze data and extract relevant patterns of information from them. Those neural networks are bifurcated into 3 mechanisms an input layer, a hidden layer, and an output layer. And when many small networks are joined together into layers, a deep neural network is created. Deep learning helps to distinguish more complex patterns and understand the data deeper to make efficient and effective decisions used in crucial AI models like self-driving cars and medical research fields.


We work Precisely to categorize, label your content with efficiency to provide qualitative end results through Entity Labeling, Entity Categorization, and Item taxonomy. Categorization is important in learning, prediction, inference, decision making, language, and many forms of organisms’ interaction with their environments.


Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. A facial recognition system uses biometrics to map facial features from a photograph or video. It compares the information with a database of known faces to find a match.


Geospatial imagery encompasses aerial, drone imagery, satellite images. We help them built-in image analysis to assess and make business-critical decisions about their business. Whether that be their mapping farm, construction site, mine, real estate project, roof or crop insurance, disaster recovery situation, or forest.


Human Powered Data Labeling is done by Data Labelers. Data Labeler specializes in providing reliable and high-quality training data sets for Machine Learning/Artificial Intelligence initiatives and the main motive is to provide a pivotal service that allows companies to focus on their core business, while we create the data sets that you need to power your algorithms.

To make companies data and algorithms successful and for accurate results, Human Data Labeling services are of utmost importance. And so being a Data Labeling company and to provide qualitative data annotation services  Our In-house, Professional, Dedicated and trained teams work with utmost accuracy to provide qualitative data labeling service. 


Item taxonomy is a structure to organize all available products in a way that customers can find what they want in the least clicks possible. 

Generally, they work as a hierarchy and put products into categories. Then tags are used to group products into each category. Attributes like color or size type are then applied to the products in each category. 


The junction tree algorithm (also known as ‘Clique Tree’) is a method used in machine learning to extract marginalization in general graphs. In essence, it entails performing belief propagation on a modified graph called a junction tree. The graph is called a tree because it branches into different sections of data; nodes of variables are the branches. 


Under Keypoint Annotation Objects in an image are labeled using points to determine the shape of it. Basically, it’s drawing dots over the object. Key point annotation is used to label facial/skeletal features, automotive parts, etc.


LIDAR stands for LIght Detection and Ranging.

LiDAR annotation is similar to image labeling apart from the difference in practice for a simple reason: It is a 3D representation on a flat-screen. In addition, humans have to deal with a huge amount of points that are not contained by particular boundaries. So, even for professional humans, it is not easy to understand which point belongs to which object, and if you zoom into the point cloud image, this difficulty becomes clear. Even for LiDAR data, annotation is mostly done using the same ideas that guide the image labeling practices, such as bounding boxes or cuboids. 


Medical image annotation service for machine learning healthcare data and big data healthcare training using semantic segmentation and polygon image annotation for organ segmentation and disease diagnosis.  Medical image data annotation supports detecting and interpreting the changes in medical images It helps the radiologist to make better decisions. Hence it helps to save time and achieve the aim with minimal errors and discrepancies and so improves the accuracy in the Medical Sector.

After terminologies, one thing you must know is the first and the most important task of AI i.e. Data Annotation & Labeling. Data Annotation & data labeling is to Label and Annotate data for machine learning and it totally depends upon human experiences 

Artificial Intelligence enhances the speed, precision, and effectiveness of human efforts. Artificial Intelligence (AI) is bringing drastic changes in technical fields, where it can be implemented to automate the system for more efficiency and performance. 

Annotate your Data with us to Get the Best AI results.