Vision and sensor-based signer-independent framework for Arabic sign language recognition / Ahmad Sami Abd Alkareem Al-Shamayleh

• Main Author: Ahmad Sami Abd Alkareem , Al-Shamayleh
• Format: Thesis
• Language: unknown
• Published: 2020
• Subjects: QA76 Computer software; T Technology (General); sami
• Online Access: http://studentsrepo.um.edu.my/14341/; http://studentsrepo.um.edu.my/14341/2/Ahmad_Sami.pdf; http://studentsrepo.um.edu.my/14341/1/Ahmad_Sami.pdf

Hearing and speech-impairment disability is widespread throughout the world. At present, 15 million people have this disability in the Arab world, and about 86% of them come from low- and middle-income countries. Meanwhile, sign language (SL) can be classified into standard Arabic sign language (ArSL) and local Arabic sign language (LArSL). ArSL is the formal standard and is the more acceptable SL in the Arab world; it is also considered as the medium of instructions for schools and universities as well as television news, shows and programmes. With the absence of usable ArSL recognition (ArSLR) platforms, hearing- and speech-impaired people tend to be isolated and face serious difficulties in communication and interaction. The focus of this thesis is to design and create a new ArSL and an ArSLR framework, which cover all ArSLR approaches and ArSL forms based on the standard criteria and consensus of SL experts. Essentially, this thesis proposes two frameworks for modelling and developing ArSLR. The first framework represents a usable static backhand and a signer-independent approach for the ArSLR of letters and number signs based on the Vision-Based Recognition (VBR) approach using a smartphone camera. The second framework represents a usable hybrid VBR and sensor-based recognition (SBR) approach for signer-independent continuous ArSLR development and evaluation using Microsoft Kinect and Smart Data Gloves. To accomplish the research objective, an extensive systematic literature review has been conducted to create research taxonomies and to identify the research gaps on ArSLR approaches and ArSL forms of signs. For the first framework, the input signs to the ArSLR framework were first split into open and closed hand signs. Then, the ArSLR framework identified suitable approaches of recognising open and closed hand signs, such as the discrete wavelet transform, which was integrated with 1D-signature signals in closed hand signs. The open hand signs were differentiated through the distribution of quantised area ...