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SCIENTIFIC UNDERSTANDING AND VISION-BASED TECHNOLOGICAL DEVELOPMENT FOR CONTINUOUS SIGN LANGUAGE RECOGNITION AND TRANSLATION (SIGNSPEAK)
Date du début: 1 avr. 2009, Date de fin: 31 mars 2012 PROJET  TERMINÉ 

Deaf communities revolve around sign languages as they are their natural means of communication. Although deaf, hard of hearing and hearing signers can communicate without boundaries amongst themselves, there is a serious challenge for the deaf community in trying to integrate into educational, social and work environments, as the vast majority of Europeans do not have signing skills. The overall goal of SignSpeaker is to develop a new vision-based technology for translating continuous sign language to text, in order to improve the communication between deaf and hearing communities. To this end, at the beginning of the project a new scientific study will be carried out to increase the linguistic understanding of sign languages; this new knowledge about the nature of sign language structure from the perspective of machine recognition of continuous sign language is crucial for a further development of sign-language-to-text technologies. The breakthrough in the understanding of sign language will allow a subsequent breakthrough in the development a new vision-based technology for continuous sign language recognition and translation to text. The SignSpeaker system will track the dominant and non-dominant hand, as well as facial expressions and body posture, taking into account the signs performed before and after or, in other words, taking into account the context in which a sign has been realised; the new technology will be also signer and ambient-independent. SignSpeaker will be the first step to approach sign language recognition and translation to the levels already obtained in similar technologies like translating from text-to-speech and speech-to-text. But the impact of SignSpeaker's results is much broader than the unique application in sign languages, because the results have also important applications in the industry for improving human-machine communication by gesture and for an automatic object and body part recognition and tracking in video streams.

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