Haravi Software Pvt. Ltd.

MAKING AI POSSIBLE WITH RIGHT DATA ANNOTATION AND IMAGE ANNOTATION SERVICES

A low-cost data annotation service for machine learning and artificial intelligence companies seeking high-quality training data for diverse businesses.

 

Welcome To Haravi Software

Our aims to augment, annotate, and label data accurately, securely, and efficiently by involving humans in the process. Having a rich background in AI, machine learning, and data processing, we are uniquely qualified to provide industry-specific workforce solutions for data annotation & labeling.

  • We annotate & label data for machine learning.
  • We process language & moderate text.
  • We develop AI training data for computer vision systems.

Our Services

We are providing data annotation for machine learning using the advance tools and human powered skills to make each image easily recognizable for machines or computer vision. We can label each data or annotate different types of objects like cars, human, animals or trees etc. using the various modes of annotation as per the client’s needs.

Semantic Segmentation

Semantic Segmentation helps to solve the vision problem in outdated computer that involves taking raw data like 2D images as an input and convert them into a mask with areas need to be highlighted. In semantic segmentation masses parts of images together belongs to the same object interested by the users and solve the computer vision problem.

 

Polygon Annotation

Polygon or Polygonal segmentation will help you to annotate irregular or coarse object that are in asymmetrical shapes. It helps to capture the large images like aerial photography for more precise annotation. Annotate your images with bounding polygons with higher-level of precision to build a sharper and correct vision models for aerial imagery.

Bounding Box Annotation

Outline the objects using bounding boxes for in depth recognition either its humans, cards or other objects on the streets. We use 2D and 3D bounding box annotation tool depending on your quantity and quality of data. We will help you train the autonomous vehicles recognize such objects with proper tagging and classification to ensure the quality.

 

Polygon Annotation

Drawing precisely with contours in polygon shapes over the products to detect the objects and localization of videos or images.

Polylines Annotation

Applied into lane detection on roads for autonomous cars and other vehicles to detect the lane and move into the right direction.

Semantic Segmentation

It is the process of image annotation by partitioning the images into multiple segments for autonomous vehicles and healthcare.

Bounding Box

A popular method of classifying objects in images or videos for retail, robotics, autonomous vehicles, Machine learning needs.

Landmark Annotation

Plotting a sequence of points correctly to determine the shape of natural objects like facial features, emotions and expressions.

3D Cuboid Annotation

Capture the object in 3D cuboid to get the more in-depth dimension view and build a more comprehensive model for detailed info.

Industries

Our Image Annotation service is spread over wide-range of industries working on AI-based or machine learning-based business models. From ecommerce to retail and healthcare, we cover most of them with same level of dedication and quality.

Self Driving

Used for computerized vision learning for autonomous vehicle driving

Health Care

The diseases diagnosis and analysis through annotated medical imaging

AI in Retail

Used for Ecommerce and retail supermarkets through vision based inspection

Autonomous Flying

Mainly used for aerial view capturing and object recognition through drones

Robotics

Used as robotic arms for material handling at warehouse or assembly plants

Security

Mainly used to keep monitoring the objects through automated security cameras

Satellite Imagery

Used for satellite images to get the view for urban management and smart cities

Agriculture

Used as robotics in agriculture for soil monitoring, crop harvesting or yield analysis