Identification of Indonesian Sign Language System Using Deep Learning in Yolo-based
DOI:
https://doi.org/10.62205/mjgcs.v1i2.22Keywords:
SIBI, Computer Vision, Yolo, Sing Language Recognition, Deep LearingAbstract
Deafness, or hearing impairment, refers to the loss of auditory capability in one or both ears. Deaf communities often develop sign languages to facilitate communication. Sign language, which employs hand movements, is commonly adopted by individuals with hearing impairments. In Indonesia, two primary sign languages are used: BISINDO (Bahasa Isyarat Indonesia) and SIBI (Sistem Isyarat Bahasa Indonesia). The main distinction between these languages is that BISINDO employs both hands for signing, whereas SIBI uses only one hand. Individuals with hearing impairments face significant communication challenges. This study focuses on the detection of alphabets in the Indonesian Sign Language System (SIBI) using YOLO v5. The objective is to recognize alphabetic characters through hand gesture signals. Experimental results indicate a detection success rate of 95.38%, accurately identifying 23 out of the 24 tested letters.
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