Sign Language Translation System Using Video Analysis
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Solution Overview
Problem
Current sign language robots can only translate voice into sign language actions, failing to translate sign language actions into text, which hinders understanding between hearing impaired and ordinary people.
Innovation Solution
A method and system that identifies sign language actions in video footage, using a sign language model and language model to translate these actions into text, enabling better communication by determining when an intention expression is complete and sending the translated text to a terminal device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sign language robots use one-to-one mapping to convert voice into sign language actions, then the conversion efficiency is improved, but the ability to translate sign language actions into text is lost
Solution Approach 1:
The sign language robot is enhanced with multiple translation capabilities: it can convert voice to sign language actions, sign language actions to text, and text to voice. This multi-functional design allows the robot to serve both hearing impaired users (voice to sign language) and ordinary users (sign language to text), resolving the limitation of unidirectional translation while maintaining efficient conversion through standardized processing pipelines.
2Loss of information
If sign language actions are translated into text in real-time, then the communication understanding is improved, but the system complexity increases
Solution Approach 1:
A camera serves as an intermediary device to capture sign language actions, which are then processed through computer vision algorithms to extract hand poses and gestures. These extracted features are fed into a translation model that converts them into text. This intermediary approach breaks down the complex translation task into manageable stages, improving communication understanding while controlling system complexity through modular architecture.
Solution Approach 2:
The patent replaces traditional mechanical sign language interpretation methods with computer vision and deep learning technologies. Instead of using complex mechanical sensors to track hand movements, the system uses image processing algorithms to automatically detect and interpret sign language gestures from video feeds, significantly reducing hardware complexity while maintaining or improving translation accuracy.
3Measurement precision
If multiple sign language actions are buffered before translation, then the translation accuracy is improved, but the response time increases
Solution Approach 1:
The system implements periodic processing where sign language actions are captured continuously but translated in periodic batches. Multiple sign language actions are buffered and then translated together in periodic intervals, allowing the system to accumulate sufficient data for accurate translation while maintaining a predictable response rhythm. This periodic approach balances accuracy requirements with timely response by translating at regular intervals rather than waiting for complete sentences.
Data Source
AI summary
The present disclosure provides a method, an apparatus, a device and a system for sign language translation, where a server receives video information sent by a terminal device, and preprocesses the video information to obtain at least one sign language action; the at least one sign language action is input into a sign language model for classification and prediction to obtain a word corresponding to the at least one sign language action; each word is input into a language model to determine whether an intention expression is complete; and each word is sent to the terminal device when the intention expression is complete, so that the terminal device displays each word, thereby realizing the translation of the sign language action into text, enabling the ordinary persons to better understand intentions of the hearing impaired, thus improving efficiency of communications.