Deep Learning Approach For Sign Language Recognition

Triwijoyo, Bambang Krismono Triwijoyo and Karnaen, Lalu Yuda Rahmani and Adil, Ahmat (2023) Deep Learning Approach For Sign Language Recognition. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), 9 (1). pp. 12-21. ISSN ISSN: 2338-3070

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Sign language is a method of communication that uses hand gestures between people with hearing loss. Each hand sign represents one meaning, but several terms don't have sign language, so they have to be spelled alphabetically. Problems occur when communicating between normal people with hearing loss, because not everyone understands sign language, so a model is needed to recognize sign language as well as a learning tool for beginners who want to learn sign language, especially alphabetic sign language. This study aims to create a hand sign language recognition model for alphabetic letters using a deep learning approach. The main contribution of this research is to produce a real-time hand sign language image acquisition, and hand sign language recognition model for Alphabet. The model used is a seven-layer Convolutional Neural Network (CNN). This model is trained using the ASL alphabet database which consists of 27 categories, where each category consists of 3000 images or a total of 87,000 hand gesture images measuring 200×200 pixels. First, the background correction process is carried out and the input image size is changed to 32×32 pixels using the bicubic interpolation method. Next, separate the dataset for training and validation respectively 75% and 25%. Finally the process of testing the model using data input of hand sign language images from a web camera. The test results show that the proposed model has good performance with an accuracy value of 99%. The experimental results show that image preprocessing using background correction can improve model performance.

Item Type: Artikel Umum
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisi / Prodi: Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro)
Depositing User: M.Eng. Alfian Ma'arif
Date Deposited: 14 Mar 2023 04:56
Last Modified: 14 Mar 2023 04:56

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