Sign Language Subtitle Generation Using LSTM Gesture Analysis

The patent replaces the mechanical manual translation process with an automated computer vision system using CNNs and LSTMs. The system automatically detects hand gestures, extracts landmarks, and translates sign language to text without human intervention, thereby eliminating time consumption while maintaining accuracy through sophisticated algorithms.

The system enables self-service by allowing the video processing system to automatically generate subtitles without requiring expert translators. The automated pipeline including gesture detection, landmark extraction, and sequence modeling creates a self-sufficient solution that eliminates dependency on manual expert intervention.

The patent uses copying by creating detailed digital representations (landmarks) of physical hand gestures from standard video footage. Instead of adding hardware sensors, the system copies the essential gesture information into a simplified landmark coordinate format that can be processed computationally, achieving high accuracy without additional hardware complexity.

The system substitutes physical hardware sensors with a software-based computer vision approach. By using CNNs to detect and track landmarks from regular video frames, the patent replaces the need for specialized hardware while maintaining gesture detection accuracy through advanced image processing techniques.

The patent applies dynamics by transitioning from static gesture recognition to dynamic sequence modeling. The system processes gestures as temporal sequences of landmarks across multiple frames using LSTMs, capturing the evolution and movement of signs over time. This dynamic approach maintains processing speed while significantly improving accuracy for dynamic gestures that span multiple frames.

The system performs preliminary action by extracting and storing landmark coordinates for each frame in advance before sequence modeling. This pre-processing step organizes the spatial-temporal data structure, enabling the LSTM to efficiently process dynamic gestures without sacrificing processing speed, as the heavy lifting of feature extraction is completed beforehand.

The patent applies extraction by isolating and focusing only on the essential landmarks (hand, face, body key points) rather than processing the entire video frame. This selective extraction reduces computational complexity by concentrating resources on the most relevant features for gesture identification, while maintaining accuracy through precise landmark detection using CNNs.

The system applies segmentation by dividing the video analysis into distinct components: landmark detection, coordinate extraction, derivative calculation, and sequence modeling. This modular segmentation allows each component to be optimized independently, reducing overall computational complexity while maintaining high gesture identification accuracy through specialized processing for each stage.