Sign language recognition plays a crucial role in bridging the communication gap between hearing-impaired and hearing individuals. However, traditional teaching systems often rely on expert systems or rule-based approaches for recognition, which struggle to meet the needs of learners at different levels. This paper introduces a novel Sign Language Teaching and Scoring System (SLTS) based on multi-model collaboration, aimed at improving learning efficiency and accuracy for diverse learners. The proposed SLTS employs teaching strategies suitable for both beginners and advanced learners, offering a comprehensive solution for sign language education through multiple representation learning. Specifically, for beginners, it uses an improved Siamese Long Short-Term Memory (LSTM) module to facilitate passive learning. This approach analyzes individual gestures by comparing them to conventional sign language, allowing novices to focus on mimicking movements and establishing a solid foundation in sign language norms. For advanced learners, the proposed SLTS implements an active learning approach using an enhanced Convolutional LSTM (ConvLSTM) module to handle more complex sign language vocabulary. The system captures both spatial and temporal features of gestures, enhancing learners' fluency and expressiveness in real communication scenarios. The experimental results in real-world environments demonstrate that the proposed SLTS significantly outperforms existing methods in recognition accuracy, proving its effectiveness and advanced nature.
