School of Engineering (SEN)

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A vision based approach for Pakistan sign language alphabets recognition
(Pensee Journal, 2014) Nabeel Sabir Khan; Adnan Shahzada; Saleem Ata; Adnan Abid; Yaser Daanial khan; Muhammad Shoaib Farooq; Muhammad Tahir Mushtaq; Inayat Khan
There is a persistent communication barrierbetween the deaf and normal community becausea normal person has no or limited fluency with thesign language. A person with hear-impairment hasto express himself via interpreters or text writing.This inability to communicate effectively between thetwo groups affects their interpersonal relationships.There are about 0.24 million Pakistanis who are eitherdeaf or mute and they communicate through PakistanSign Language(PSL). In this research work a systemfor recognizing hand gestures for Pakistan Sign Languagealphabets in unimpeded environment is proposed. A digitalcamera is used to acquire PSL alphabet’s images withrandom background. These images are preprocessedfor hand detection using skin classification filter. Thesystem uses discrete wavelet transform (DWT) for featureextraction.Artificial neural network(ANN) withbackpropagation learning algorithm is employed torecognize the sign feature vectors. The dataset contains500 samples of Pakistan Sign Language alphabets withvarious background environments. The experimentsshow that the classification accuracy of the proposedsystem for the selected PSL alphabets is 86.40%.