Researchers also use many other approaches for sign recognition. To process the data collected through the devices, researchers implemented neural networks such as the Stuttgart Neural Network Simulator for pattern recognition in projects such as the CyberGlove. However, with the development of computer vision, wearable devices were replaced by cameras due to their efficiency and fewer physical restrictions on signers. The wearable hardware made it possible to capture the signers' hand shapes and movements with the help of the computer software. Later, the use of gloves with motion sensors became the mainstream, and some projects such as the CyberGlove and VPL Data Glove were born. In 1977, a finger-spelling hand project called RALPH (short for "Robotic Alphabet") created a robotic hand that can translate alphabets into finger-spellings. The history of automatic sign language translation started with the development of hardware such as finger-spelling robotic hands. There is no gold standard parallel corpus that is large enough for SMT, for example. Sign Languages then are recorded in various video formats. There are notations systems but no writing system has been adopted widely enough, by the international Deaf community, that it could be considered the 'written form' of a given sign language. An additional challenge for sign language MT is the fact that there is no formal written format for signed languages. This multi-channel articulation makes translating sign languages very difficult. Where spoken languages are articulated through the vocal tract, signed languages are articulated through the hands, arms, head, shoulders, torso, and parts of the face. This is, in no trivial way, due to the fact that signed languages have multiple articulators. In fact, sign language translation technologies are far behind their spoken language counterparts. Sign language translation technologies are limited in the same way as spoken language translation. Developers use computer vision and machine learning to recognize specific phonological parameters and epentheses unique to sign languages, and speech recognition and natural language processing allow interactive communication between hearing and deaf people. Sign languages possess different phonological features than spoken languages, which has created obstacles for developers. These technologies translate signed languages into written or spoken language, and written or spoken language to sign language, without the use of a human interpreter. When a research project successfully matched English letters from a keyboard to ASL manual alphabet letters which were simulated on a robotic hand. The machine translation of sign languages has been possible, albeit in a limited fashion, since 1977. ( December 2021) ( template removal help) Please improve this article by removing excessive or inappropriate external links, and converting useful links where appropriate into footnote references. This article's use of external links may not follow Wikipedia's policies or guidelines.
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