Data Annotation in AI
Services / Data Annotation in AI
Image Labelling
    • Bounding Boxes
    • Polygonal Selection
    • Cuboid Selection
    • Attributes Tagging
Video Annotations
    • Multiple Shapes in same scene
    • Auto Identification of Classes
    • Localization Support
    • Image Segmentation
Text Decoding
    • OCR and ICR
    • Decoding and Transcription
    • Comparison and De-duplication
    • Classification and Extraction
Autonomous Vehicles and ADAS Data Annotation
    • Semantic Segmentation
    • Bounding Boxes
    • Lane Identification
    • Multiple Object Tracking
    • Attributes Tagging

Applicability across wide range of verticals Retail, Automotive, Healthcare, BFSI, Manufacturing, Enterprise, Governance etc.

ANNOTATION

Healthcare

Finance

Automotive

Manufacturing

Governance

We work on Enterprise platforms as well as our in-house platform to perform a versatile range of work from Labelling to Ground Truth Dataset creation.

ANNOTATION
  • Perform quality annotation of all forms of Data, Image, Video and Text, to produce ground truth dataset
  • Annotate the Core Data and related Characteristics and Attributes.
  • Enrich Data Dictionary
RECOGNITION
  • Train the model with quality data set to ensure accurate recognition of Objects (Static or Moving), Image, Products, Location etc.
  • Increment Model Accuracy with manual validation.
SEGMENTATION
  • Reduce noise by segmenting the required data from a complex image to ensure availability of relevant dataset.
  • Label complex images pixel-by-pixel level granularity to generate pre-determined object classes and produce meaningful information
TRANSCRIPTION
  • Image Transcription and Optical Character Recognition (OCR), ICR and integrated established machine learning models to ensure accuracy
  • Support for Structured or Unstructured Text Decoding with Manual touch points.
COMPARISON
  • Perform scalable comparison and de-duplication to ensure good quality and unique annotations, segmentations are available, noise is filtered and redundancy is reduced.
  • Aids in ground truth dataset production for model training and validation
CLASSIFICATION
  • Perform tagging of Images, Objects (Static or Moving), Text, Content Moderation to categorize it pre-defined product categories
  • High volume of dataset classified through manual tagging and automated recognition engine.

A typical cycle of Data Curation and Enrichment in AI and Machine Learning is as below. Human in the loop and Human Intelligence play a vital role in the journey to verify, validate and fix issues in model outcome so that further efficiency and improvisation can be achieved.

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