A Proposed Vision Purposive Architecture for Arabic Sign Language Recognition using Deep Learning Paradigm
Menna Tu-Allah Ahmed ElBadawy;
Abstract
Abstract
Arabic Sign Language ArSL is widely used in the Arabian countries due to its facilities to communicate with Hearing Impaired HI individuals. ArSL Recognition becomes vital for communication among HI persons and many technologies were employed to serve the recognition purpose and a lot of researches had been conducted to study either static or dynamic gestures.
Sign Language Recognition breaks the barrier between deaf and normal people. As the only way to communicate with HI persons is acting the meaning of words by hands and body, this way will deliver the meaning for either the normal or HI people. After increasing of Sign Language usage and importance, translation system for such languages become an essential need and also the requirement for a standard dictionary for ArSL.
The main purpose of the thesis is to develop an Arabic Sign Language Recognition ArSLR system which translates ArSL to Arabic words using deep techniques. The dataset which is used in our system is taken from the standard Arabic Sign Language dictionary published in 2005 (Samreen و Benali، 2009). The words are represented in 40 postures those were captured and collected from different signers and in different environments. The dataset was recorded with a digital camera from different angles.
Arabic Sign Language ArSL is widely used in the Arabian countries due to its facilities to communicate with Hearing Impaired HI individuals. ArSL Recognition becomes vital for communication among HI persons and many technologies were employed to serve the recognition purpose and a lot of researches had been conducted to study either static or dynamic gestures.
Sign Language Recognition breaks the barrier between deaf and normal people. As the only way to communicate with HI persons is acting the meaning of words by hands and body, this way will deliver the meaning for either the normal or HI people. After increasing of Sign Language usage and importance, translation system for such languages become an essential need and also the requirement for a standard dictionary for ArSL.
The main purpose of the thesis is to develop an Arabic Sign Language Recognition ArSLR system which translates ArSL to Arabic words using deep techniques. The dataset which is used in our system is taken from the standard Arabic Sign Language dictionary published in 2005 (Samreen و Benali، 2009). The words are represented in 40 postures those were captured and collected from different signers and in different environments. The dataset was recorded with a digital camera from different angles.
Other data
| Title | A Proposed Vision Purposive Architecture for Arabic Sign Language Recognition using Deep Learning Paradigm | Authors | Menna Tu-Allah Ahmed ElBadawy | Issue Date | 2018 |
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