Sign-Language Translator
Scientists develop first sign-language dictionary searchable by gesture.
Jan. 12, 2008— -- Bilingual dictionaries are usually a two-way street: you can look up a word in English and find, say, its Spanish equivalent, but you can also do the reverse. Sign-language dictionaries, however, translate only from written words to gestures. This can be hugely frustrating, particularly for parents of deaf children who want to understand unfamiliar gestures, or deaf people who want to interact online using their primary language. So Boston University (BU) researchers are developing a searchable dictionary for sign language, in which any user can enter a gesture into a dictionary's search engine from her own laptop by signing in front of a built-in camera.
"You might have a collection of sign language in YouTube, and now to search, you have to search in English," says Stan Sclaroff, a professor of computer science at BU. It's the equivalent, Sclaroff says, of searching for Spanish text using English translations. "It's unnatural," he says, "and it's not fair."
Sclaroff is developing the dictionary in collaboration with Carol Neidle, a professor of linguistics at BU. Once the user performs a gesture, the dictionary will analyze it and pull up the top five possible matches and meanings.
"Today's sign-language recognition is [at] about the stage where speech recognition was 20 years ago," says Thad Starner, head of the Contextual Computing Group at the Georgia Institute of Technology. Starner's group has been developing sign-language recognition software for children, using sensor-laden gloves to track hand movements. He and his students have designed educational games in which hearing-impaired children, wearing the gloves, learn sign language. A computer evaluates hand shape and moves on to the next exercise if a child has signed correctly.
Unlike Starner's work, Sclaroff and Neidle's aims for a sensorless system in which anyone with a camera and Internet connection can learn sign language and interact. The approach, according to Starner, is unique in the field of sign-language recognition, as well as in the field of computer vision.
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