Deep-rsl

Abstract

In conclusion, this document has made significant progress in addressing the challenges of recognizing Algerian Sign Language (LSA) and has contributed to the development of effective LSA recognition systems. Through our research, we have achieved notable milestones and gained valuable insights. One of the major accomplishments of this document is the successful development of a real-time application that accurately interprets LSA gestures. Our work involved creating five variants and extensively testing each one to determine the most effective approach. We specifically focused on frame extraction from videos, allowing us to extract significant frames for model input, ensuring real-time processing and response. By employing machine learning techniques, particularly an autoencoder with attention, we achieved a balance between accuracy and response time, resulting in a recognition system with high reliability and efficient performance. Our rigorous evaluation and comparison of the different variants allowed us to select the optimal approach for LSA recognition. The extensive dataset collection, involving visits to deaf schools across Algeria and collaboration with the deaf community, has been instrumental in training and evaluating our recognition models. This diverse dataset, combined with the selected frame extraction method, ensured the robustness and scalability of our system. The involvement of the deaf community throughout the research process has been invaluable. Their expertise, feedback, and cultural insights have guided our work, ensuring the accuracy and cultural authenticity of the collected data. By actively involving them, we have created a recognition system that aligns with their needs and preferences, ultimately enhancing communication and accessibility for the deaf community. Looking ahead, there are several perspectives to consider for further research: • Dataset Expansion: To further improve the accuracy and generalizability of LSA recognition systems, expanding the dataset is crucial. Collecting additional diverse samples, including a wider range of LSA signs, gestures, and expressions, would enhance the system's performance and adaptability.92 | • Integration of Facial Expression and Emotion Recognition: To provide a more comprehensive understanding of sign language communication, incorporating facial expression and emotion recognition as additional features would enrich the interpretation of LSA gestures. This can enhance the system's ability to capture nuanced expressions and improve overall accuracy. • Continuous Sign Language Recognition: Building upon our work with isolated gestures, exploring continuous sign language recognition is essential. Developing models that can interpret complete sign language sentences and conversations would enable more natural and fluid communication for the deaf community, extending the system's usability and impact. In summary, this document has contributed to the advancement of LSA recognition systems by developing a real-time application with optimized frame extraction and machine learning techniques. Our collaboration with the deaf community and careful evaluation of different variants ensured the accuracy, reliability, and cultural authenticity of our system. As we move forward, expanding the dataset, incorporating facial expression recognition, and exploring continuous sign language recognition will further enhance the effectiveness and inclusivity of LSA recognition systems.