SignSense- Empowering Communication through Sign Language Understanding

SignSense- Empowering Communication through Sign Language Understanding

Use Case Description

Problem Statement: The goal of sign language detection is to develop a computer vision-based system that can accurately recognize and interpret sign language gestures performed by users. This enables efficient communication between hearing-impaired individuals and the general population, bridging the communication gap and promoting inclusion.

Solution: The proposed solution involves using UiPath Stacks, computer vision techniques, and TensorFlow. The system captures real-time video input from a camera, preprocesses it, and feeds it to a trained deep learning model for sign language classification. The model then predicts the corresponding sign and displays it as text or as an animated sign on-screen. The system’s accuracy and performance are continuously improved through data augmentation, model fine-tuning, and user feedback.

AS-IS WORKFLOW, TO-BE WORKFLOW

Other information about the use case

Industry categories for this use case: Healthcare Pharma, Information Technology and Services, Marketing Sales, Universities Academy, Public Sector

Skill level required: Advanced

UiPath Products that were used: UiPath Studio, UiPath AI Center, UiPath Apps, UiPath Assistant, UiPath Orchestrator, UiPath Task Capture, UiPath AI Computer Vision

Other applications that were used: Python Tensor flow Library

Other resources: -

What is the top ROI driver for this use case?: Other

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