Computer
Raja’a M. Mohammed; Suhad M. Kadhem
Abstract
Sign language (SL) is Non-verbal communication and a way for thedeaf and mute to communicate without words. A deaf and mute person's hands,face, and body shows what they want to say. Since the number of deaf and dumbpeople is increasing, there must be other ways to learn sign language orcommunicate with ...
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Sign language (SL) is Non-verbal communication and a way for thedeaf and mute to communicate without words. A deaf and mute person's hands,face, and body shows what they want to say. Since the number of deaf and dumbpeople is increasing, there must be other ways to learn sign language orcommunicate with deaf and dumb people. One of these ways is using advancedtechnology to produce systems that help the deaf/dumb, such as creatingrecognition and sign language translators. This paper presents an applicationthat works on the computer for machine translation of Iraqi sign language intwo directions from sign language to Arabic language (text/speech) and fromArabic language(text) to Iraqi sign language. The proposed system uses aConvolution Neural Network (CNN) to classify sign language based on itsfeatures to predicate the sign meaning. The sign language to Arabiclanguage(text/speech) part of the proposed system has an accuracy of 99.3% forletters.
Hussein Ali Aldelfy; Mahmood Hamza Al-Mufraji; Thamir R. Saeed
Volume 20, Issue 2 , April 2020, , Page 23-32
Abstract
Sign language is the basic way to communicate and understand ideas among people with disabilities, deaf and dumb, and to recognize and understand the meanings of the motions and the gestures used by them that requires knowledge of certain features such as hand position, form, motion, directions and facial ...
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Sign language is the basic way to communicate and understand ideas among people with disabilities, deaf and dumb, and to recognize and understand the meanings of the motions and the gestures used by them that requires knowledge of certain features such as hand position, form, motion, directions and facial expressions. Moreover, the extraction of image features is important and it plays a vital role in further analysis of image processing. Extracting the special features of the isolated Arabic sign language (ArSL) words based on Discrete Cosine Transform (DCT) was proposed in current paper. By monitoring and tracking the trajectory of the hands when single hand movement or double hands extract the required features. That is the independent sign language word features vector is incorporated as an input into the classification stage to derive the meaning of the word as voice or text. The research data were collected in cooperation with the Ministry of Labor and Social Affairs of Iraq with the assistance of the Special Needs Department and up to 40 words of isolated sign language collected. Two signs were taken as examples of feature extraction (Happy and water). Since the word happy gives an example of a sign word performed by two hands, on the other hand, the word water is performed with one hand, the Arabic sign language contains different words, part of them are performed with one hand and the other part with both hands.
