MBSAP HUMAN GESTURE ANNOTATION SERVICES

Artificial Intelligence (AI) has made great leaps forward in the last few decades. Collaborating with human intelligence is the key to build reliable AI systems, as we need to filter large volumes of data sample to train machines to derive better and accurate insights. Human gesture annotation is a relatively new and challenging technology, which helps businesses build a robust pattern recognition system to aid machine learning.

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    Human Gesture Annotation Services We Offer

    As a global leader, we at Mbsap handpick industry experts to ensure high-quality human gesture annotation services to help you transform your machine learning ideas into reality. Working with you as an extension of your in-house team, our data scientists and researchers bring in innovative analysis methods to extract off-the-shelf data for higher activity recognition. Helping you avoid large expenses on computational resources with cumbersome image annotation requirements, we provide a professional touch to ensure quality data accumulation and maximum utilization for better machine learning.

    Right from posture to gesture recognition, we at Mbsap help you improve your AI and machine learning products to provide higher customer experience with high-volumes of right data to train machines to imitate human thought and action related to human gesture. Our annotated data services enable clients to create richer, more authentic, more valuable, and more functionally usable applications. Some of our capabilities include –

    • Comprehensive classification of tagging gestures as well as facial expressions for objective quantification. We consider pictorial, spatial, rhythmic, kinetic, pointing gesture annotation, etc. to aide our clients. Along with that we help you study redundant, content-carrying, and enhancing gestures to avoid information overlap
    • Semantic annotation of text that includes entity identification for sentiment analysis, data mining and search applications
    • We provide transcription as well as time-stamping to accurately describe image content to train machines helping clients optimize training models and support their goals
    • Better quality machine training material with human-annotated data based on subjectivity, intent, and lack of ambiguity