Prashant Chaudhary
Helping companies build AI with high-quality data
New to Contra
Prashant is ready for their next project!
Annotated computer vision datasets used for vehicle detection and Automatic Number Plate Recognition (ANPR) systems.
The project involved labeling vehicles and number plates in traffic camera footage to help train AI models used in smart city infrastructure, traffic monitoring, and law enforcement systems.
Key responsibilities included:
• Vehicle detection using bounding box annotation
• Number plate annotation for ANPR systems
• Dataset preparation for computer vision models
• Annotation quality validation and dataset review
These datasets support AI models used in traffic monitoring systems, automated toll collection, smart parking systems, and urban mobility analytics.
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Worked on Automatic Speech Recognition (ASR) dataset annotation used for training speech-to-text AI models.
The project involved labeling and segmenting audio recordings, identifying speakers, and preparing structured datasets for machine learning systems.
Key responsibilities included:
• Audio segmentation and timestamp labeling
• Speaker identification and classification
• Speech-to-text dataset preparation
• Annotation quality validation and review
This dataset helps train AI systems used in voice assistants, speech recognition software, and conversational AI applications.
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Annotated CT scan medical imaging datasets used to train AI models for healthcare diagnostics and medical image analysis.
The project involved identifying and labeling anatomical regions within CT scan images using bounding boxes and segmentation techniques to support machine learning model training.
Key tasks included:
• Medical image annotation and region labeling
• Bounding box annotation for anatomical structures
• Dataset preparation for AI model training
• Quality control and annotation validation
This work supports the development of AI systems used in healthcare diagnostics, medical imaging analysis, and clinical decision support systems.
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Annotated complex dental X-ray medical imaging datasets used to train AI models for healthcare applications.
The project involved precise labeling of anatomical structures including teeth boundaries, roots, and surrounding regions using polygon and keypoint annotation techniques.
The objective was to generate high-quality training datasets that allow machine learning systems to accurately detect dental structures and support medical imaging analysis.
Responsibilities included:
• Image segmentation and polygon annotation
• Keypoint labeling for anatomical structures
• Dataset preparation for machine learning models
• Annotation quality control and validation
This work contributes to the development of AI systems used in medical diagnostics and healthcare imaging analysis.
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