11/23/2023 0 Comments Asl gloss translatorMoreover, the accurate sign language recognition significantly affects the performance of sign language translation and representation methods. The accurate extraction of hand and body motions as well as facial expressions plays a crucial role to the success of the machine learning algorithms that are responsible for the robust recognition of signs. Sign language technologies are connected in a way that affect each other as seen in Figure 1. Currently, avatar movements are deemed unnatural and hard to understand by the Deaf community due to inaccuracies in skeletal pose capturing and the lack of life-like features in the appearance of avatars. Finally, sign language representation involves the accurate representation and reproduction of signs using realistic avatars or signed video approaches. The difficulties in SLT lie in the lack of multilingual sign language datasets, as well as the inaccuracies of SLR methods, considering that the gloss recognition (performed by SLR methods) is the initial step of the SLT methods. SLT methods employ sequence-based machine learning algorithms and aim to bridge the communication gap between people signing or speaking different languages. On the other hand, sign language translation (SLT) involves the translation between different sign languages, as well as the translation between sign and speaking languages. Current limitations in SLR lie in the lack of large annotated datasets that greatly affect the accuracy and generalization ability of SLR methods, as well as the difficulty in identifying sign boundaries in continuous SLR scenarios. Sign language recognition (SLR) involves the development of powerful machine learning algorithms to robustly classify human articulations to isolated signs or continuous sentences. The accuracy of sign language capturing technologies is currently limited by the resolution and discrimination ability of sensors and the fact that occlusions and fast hand movements pose significant challenges to the accurate capturing of signs. More specifically, sign language capturing involves the accurate extraction of body, hand and mouth expressions using appropriate sensing devices in marker-less or marker-based setups. Sign language technologies cover a wide spectrum, ranging from the capturing of signs to their realistic representation in order to facilitate the communication between hearing-impaired people, as well as the communication between hearing-impaired and speaking people. Although the development of such technologies can be really challenging due to the existence of numerous sign languages and the lack of large annotated datasets, the recent advances in AI and machine learning have played a significant role towards automating and enhancing such technologies. The research community has long identified the need for developing sign language technologies to facilitate the communication and social inclusion of hearing-impaired people. Being able to capture and understand the relation between utterances and words is crucial for the Deaf community in order to guide us to an era where the translation between utterances and words can be achieved automatically. These features are combined together to form utterances that convey the meaning of words or sentences. Sign language (SL) is the main means of communication between hearing-impaired people and other communities and it is expressed through manual (i.e., body and hand motions) and non-manual (i.e., facial expressions) features.
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