Modular Sign-Language Classifiers for Accurate Translation
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Solution Overview
Problem
Existing methods for translating sign language into spoken language face challenges due to the dynamic and visual nature of sign languages, including difficulties in differentiating between various sign categories, capturing motion and hand shapes, and adapting to individual variations or regional dialects, leading to inaccurate translations.
Innovation Solution
A modular sequential classifier system that segregates and processes different types of signs (single, fingerspelled, and regular) using specialized classifiers, enhanced by brightness adjustment, pose modeling, and synthetic training data, with user customization options for personalized signs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single unified classifier is used to recognize all sign types, then the device complexity is reduced, but the measurement precision of sign recognition deteriorates due to inability to capture unique characteristics of different sign categories
Solution Approach 1:
The patent divides the sign recognition task into multiple specialized classifiers: a first classifier for discrete single signs, a second classifier for fingerspelled signs, and a third classifier for regular continuous signs. Each classifier is trained specifically on its designated sign type, allowing the system to capture unique characteristics of each category while maintaining manageable complexity through modular architecture.
2Measurement precision
If brightness adjustment and pose modeling are applied to enhance sign capture, then the measurement precision of hand shape and motion recognition is improved, but the use of energy and computational resources increases
Solution Approach 1:
The system performs brightness adjustment and pose modeling as preliminary preprocessing steps before the main classification task. By enhancing the visual quality and extracting key pose features in advance, the subsequent classifiers can operate more efficiently with pre-processed data, reducing the overall computational burden during the main recognition phase.
3Adaptability or versatility
If synthetic training data is generated to expand the training dataset, then the adaptability of the model to individual variations and regional dialects is improved, but the loss of time for data preparation and model training increases
Solution Approach 1:
The patent generates synthetic training data by copying and transforming existing real sign language data. The synthetic data maintains the essential characteristics of authentic signs while introducing variations in lighting, pose, and background. This approach allows the model to learn robust features and adapt to individual variations without requiring extensive manual collection of diverse training samples, significantly reducing data preparation time.
Data Source
AI summary
System and techniques to facilitate the translation of a sign language into another language are described herein. A modular architecture may be used in which the output of different classifiers may be used to produce intermediate representations, or final translations, of the sign language. These classifiers may be trained on different types of signs to enhance accuracy while reduce training time and complexity.