The use of machine learning (ML) and artificial intelligence (AI) has shown great potential in improving sign language recognition for the hearing impaired community. By leveraging large datasets of sign language videos, these technologies can help to develop more accurate and efficient recognition systems that can greatly enhance communication and accessibility. However, there are still significant challenges that need to be addressed, such as the lack of standardized sign language and the need for real-time recognition. Despite these challenges, by conducting more study and development in this area, it will be possible to create a society where everyone has equal access to knowledge and communication, regardless of language or aptitude. This paper reviews the advances in Artificial Intelligence and Machine Learning in sign language recognition, focusing on Russian, and Bengali sign languages, highlighting the potential benefits and challenges of these technologies. Both static and dynamic signs are used to improve the sign language recognition methods. While the result demonstrates approximately 94% accuracy for the static signs with convolutional neural network models, the dynamic sign recognition not only shows lower accuracy but also highlights the significance of using hybrid methods to overcome issues related to frame rate, alignment, and other aspects of video datasets.
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Ashrafi et al. (2024) studied this question.