14. An apparatus, comprising: an input device to receive data representing a string of images, the string of images representing at least part of a user; and at least one processor to process the data to detect a string of gestures of the user from the images, each gesture corresponding to a vector, map the vectors to a string of phonetic elements, and automatically identify words in a written language, the identifies words corresponding to the string of the phonetic elements; wherein the written language is a one of a plurality of regional languages, the at least one processor is to map the vectors to the string of phonetic elements by selecting each element in the string of phonetic elements in a manner that is agnostic to any one of the plurality of regional languages, an the at least one processor is to automatically identify the words in the written language by selecting a contextual dictionary specific to a selected one of the plurality of regional languages, the selected one corresponding to the written language, and translating the string of phonetic elements to the selected language; and wherein further each vector comprises a position in n-degree space, the at least one processor is to map the vectors by translating the position in n-degree space to a position in m-degree space, where n>m, and selecting a phonetic element uniquely associated with the position in m-degree space, the translating is performed by accessing a dictionary to map positions in n-degree space to corresponding positions in m-degree space, and the at least one processor is further to use principal components analysis to adaptively learn the dictionary.
14. An apparatus, comprising: an input device to receive data representing a string of images, the string of images representing at least part of a user; and at least one processor to process the data to detect a string of gestures of the user from the images, each gesture corresponding to a vector, map the vectors to a string of phonetic elements, and automatically identify words in a written language, the identifies words corresponding to the string of the phonetic elements; wherein the written language is a one of a plurality of regional languages, the at least one processor is to map the vectors to the string of phonetic elements by selecting each element in the string of phonetic elements in a manner that is agnostic to any one of the plurality of regional languages, an the at least one processor is to automatically identify the words in the written language by selecting a contextual dictionary specific to a selected one of the plurality of regional languages, the selected one corresponding to the written language, and translating the string of phonetic elements to the selected language; and wherein further each vector comprises a position in n-degree space, the at least one processor is to map the vectors by translating the position in n-degree space to a position in m-degree space, where n>m, and selecting a phonetic element uniquely associated with the position in m-degree space, the translating is performed by accessing a dictionary to map positions in n-degree space to corresponding positions in m-degree space, and the at least one processor is further to use principal components analysis to adaptively learn the dictionary. 17. The apparatus of claim 14 , wherein the at least part of the user includes a hand, wherein each image comprises information sufficient to determine a pose of the hand, and wherein each vector represents a pose of the hand.