ASL Gesture Recognition Neural Network for Real-Time Translation
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Solution Overview
Problem
Hearing-impaired individuals face challenges in participating in oral-based communication methods like videoconferences and audio calls, as existing technologies lack efficient means to translate sign language gestures into text or speech in real-time.
Innovation Solution
A computer-implemented system using deep learning and OpenCV for recognizing American Sign Language (ASL) gestures from digital video images, converting them into text or speech, and vice versa, enabling two-way communication through machine learning models and convolutional neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If real-time translation of sign language gestures into text or speech is implemented, then communication accessibility for hearing-impaired individuals is improved, but system complexity increases
Solution Approach 1:
The system divides the sign language recognition task into separate functional modules: hand gesture detection, gesture classification, and text/speech synthesis. This segmentation allows each module to be optimized independently while working together to provide real-time translation, resolving the contradiction between accessibility and complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer that converts sign language gestures into intermediate representations (gesture codes) before translating to text or speech. This intermediary layer simplifies the overall system architecture by creating a standardized interface between gesture recognition and output generation, making the system more manageable while maintaining real-time translation capability.
2Measurement precision
If high accuracy in sign language gesture recognition is achieved, then translation reliability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing video frames to extract hand region images and preparing gesture templates in advance. This preliminary processing reduces the computational burden during real-time recognition, allowing high accuracy to be maintained while minimizing processing time delays.
Solution Approach 2:
The patent applies partial action by focusing recognition resources only on the hand region rather than the entire video frame. By extracting and analyzing only the relevant hand portions, the system achieves high gesture recognition accuracy without the processing time penalty of analyzing all visual data, thus resolving the accuracy-time contradiction.
Data Source
AI summary
Computer-implemented techniques are provided for machine recognition of gestures and transformation of recognized gestures to text or speech, for gestures communicate in a sign language such as American Sign Language (ASL). In an embodiment, a computer-implemented method comprises: storing a training dataset comprising a plurality of digital images of sign language gestures and an alphabetical letter assigned to each digital image of the plurality of digital images, training a neural network using the plurality of digital images of sign language gestures as input and the alphabetical letter assigned to each digital image as output, receiving a particular digital image comprising a particular sign language gesture, and using the trained neural network to classify the particular digital image as a particular alphabetical letter.