Supervised learning classifiers for Arabic gestures recognition using Kinect V2

Abstract

Human-Computer Interaction (HCI) refers to the interaction between the computers and human. One of the most important applications of (HCI) is sign language recognition. Several research works aimed to interpret and translate the sign language to a spoken language to help the hear impaired persons in integrating with communities. Sign language is the main way of communication for the deaf persons and hearing impaired which enable them to communicate with their societies and between each other's. According to the World Health Organization, there are 466 million hearing loss people (i.e. 5% of the world population), 432 million or 93% of them are adults and 34 million or 17% of them are children. The hearing-impaired persons often need to the same level of mental capability as the normal persons. The main problem is the most of the hearing persons who cannot understand the sign language and most of hearing impaired cannot read or write our spoken language, this represents a barrier between the deaf persons and their societies so developing an automatic sign language recognition system is very necessary. This research introduces dynamic Arabic Sign Language recognition system using Microsoft Kinect. The recognition depends on using two machine learning algorithms (a) Decision Tree and (b) Bayesian Network then applied Ada-Boosting technique to enhance the recognition of the system, we compared the results with two direct matching techniques: (a) Dynamic Time Wrapping and Hidden Markov Model the system was applied on 42 Arabic gestures related to medical field. The experimental results showed that the proposed system recognition rate reached 91.18% for Decision Tree classifier, 92.50% for Bayesian classifier and 93.7% after applying Ada-Boosting.

Detection of arabic sign language by machine learning techniques with PCA and LDA

“…Algorithm NB is based on probability by defining a set of probabilities. Probabilities can be defined by calculating the frequency and sets of values in a specific data set [34]. The value of the variable class applies the Bayesian method and assumes that all attributes are independent.…”

Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition

“…According to the research of Hisham and Hamouda [58], ArSL may be identifed in real time using Microsoft Kinect. Te system's recognition skills have been vastly improved using a decision tree, a Bayesian network, and Ada-boosting.…”

Gesture based Arabic Sign Language Recognition for Impaired People based on Convolution Neural Network

“…The Architecture for Arabic Sign Language Communication System is a proposed solution that serves as a low-cost multiple languages translation. A sign languagebased communications network, all while retaining high precision and economical usability [7]. The following Fig.…”

“…Using a Hidden Markov Model (HMM) quantifier, the researchers achieve 93% accuracy for a sample of 300 words. In comparison to HMM, they use KNN and Bayesian classifications [7], which produce equivalent results. This presents a network matching method for continual detection of Arabic Sign Language sentences.…”