Visual Sign Language Translation With Context-Aware Two-Way Output
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Solution Overview
Problem
Current methods lack a real-time, accurate, and efficient two-way communication channel for individuals with hearing and speech disabilities to interact with normal individuals, requiring normal individuals to be familiar with sign language for effective communication.
Innovation Solution
A system utilizing a Deep Convex Network (DCN) model and context-based natural language processing (NLP) techniques, combined with augmented reality, to translate sign language to text and vice versa in real-time, enabling two-way communication through video conferencing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sign language translation system is implemented, then communication accessibility for PwD is improved, but system complexity increases
Solution Approach 1:
The system is divided into three independent modules: Deep Convex Network for sign language recognition, Context-based NLP for meaning interpretation, and Augmented Reality for visual feedback. Each module processes specific tasks separately, reducing overall system complexity while maintaining comprehensive functionality.
Solution Approach 2:
The patent introduces an intermediary processing layer that converts complex sign language gestures into intermediate representations, then processes them through NLP techniques to extract contextual meaning, finally translating to target language. This intermediary approach simplifies the translation process between diverse communication modes.
2Speed
If real-time translation is achieved, then communication speed is improved, but processing accuracy may deteriorate
Solution Approach 1:
The system performs preliminary processing by capturing multiple frames of sign language gestures and pre-processing them through the Deep Convex Network before NLP interpretation. This preliminary action allows sufficient processing time while maintaining real-time communication flow, preventing accuracy loss due to rushed processing.
Solution Approach 2:
The Augmented Reality component provides visual feedback to users, displaying recognized gestures and their interpretations in real-time. This feedback mechanism allows for immediate verification and correction, ensuring translation accuracy while maintaining communication speed through continuous monitoring and adjustment.
3Loss of information
If context-based NLP is applied, then understanding of meaning is improved, but computational requirements increase
Solution Approach 1:
The Context-based NLP module focuses computational resources on analyzing only the critical contextual elements of sign language gestures rather than processing all possible features uniformly. By identifying and prioritizing locally important contextual information, the system achieves comprehensive meaning understanding with reduced computational energy requirements.
Data Source
AI summary
The present invention relates to Smart visual sign language translator, interpreter, and generator. The invention is a process of two-way real time communication between person with hearing and/or speech disabilities (PwD) and normal person, wherein sign language of PwD (100) is captured as video, converted into image frames, interpreted using DCN trained model (104) and context based Natural language generation (105); and then converted into text (106) and speech (107) for a normal person to understand; similarly, communication of normal person is captured using mic (108), and is converted to text using speech to text converter (109) wherein speech recognition is done using Deep Stack Network. Module of Text and context analysis using Natural Language Understanding (110) analyses text received from (109). Interpreted text from (110) is used for predicting text to nearest sign image. These sign images are used for creating meaningful sequence of sign language gestures in (112).