Image-Based and Sensor-Based Approaches to Arabic Sign Language Recognition

Mohandes, M.; Deriche, Mohamed; Liu, Junzhao

doi:10.1109/thms.2014.2318280

Cited by 107 publications

(55 citation statements)

References 40 publications

(36 reference statements)

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“…The first was a naive Bayes classifier with an accuracy of 98.3%, and the second was a multilayer perceptron with an accuracy of 99.1%. In their conclusion, they recommended the use of two sensors for more accuracy [15]. Ahmed et al in [16] developed a system for sign-language recognition.…”

Section: Input (Via Sensors)mentioning

confidence: 99%

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Comparison of Four SVM Classifiers Used with Depth Sensors to Recognize Arabic Sign Language Words

Almasre

Al-Nuaim

2017

Computers

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Abstract:The objective of this research was to recognize the hand gestures of Arabic Sign Language (ArSL) words using two depth sensors. The researchers developed a model to examine 143 signs gestured by 10 users for 5 ArSL words (the dataset). The sensors captured depth images of the upper human body, from which 235 angles (features) were extracted for each joint and between each pair of bones. The dataset was divided into a training set (109 observations) and a testing set (34 observations). The support vector machine (SVM) classifier was set using different parameters on the gestured words' dataset to produce four SVM models, with linear kernel (SVMLD and SVMLT) and radial kernel (SVMRD and SVMRT) functions. The overall identification accuracy for the corresponding words in the training set for the SVMLD, SVMLT, SVMRD, and SVMRT models was 88.92%, 88.92%, 90.88%, and 90.884%, respectively. The accuracy from the testing set for SVMLD, SVMLT, SVMRD, and SVMRT was 97.059%, 97.059%, 94.118%, and 97.059%, respectively. Therefore, since the two kernels in the models were close in performance, it is far more efficient to use the less complex model (linear kernel) set with a default parameter.

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Section: Input (Via Sensors)mentioning

confidence: 99%

“…Mohandes, Deriche, and Liu in [15] used an LMC device in a system to recognize signs of the Arabic alphabet. A single volunteer gestured the 28 letters of the Arabic alphabet 10 times each, for a total of 280 (10 × 28) frames of data.…”

Section: Related Workmentioning

confidence: 99%

Comparison of Four SVM Classifiers Used with Depth Sensors to Recognize Arabic Sign Language Words

Almasre

Al-Nuaim

2017

Computers

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“…Feature normalization was computed by using Equation (1). The purpose is to guarantee all features have proportional range.…”

Section: Normalizationmentioning

confidence: 99%

“…The computation in sensor-based approach is not as expensive as in imagebased approach. Nevertheless, in this approach user need to wear cumbersome devices such as gloves [1].…”

Section: Introductionmentioning

confidence: 99%

Alphabet Sign Language Recognition Using Leap Motion Technology and Rule Based Backpropagation-Genetic Algorithm Neural Network (Rbbpgann)

Khotimah¹,

Saputra²,

Suciati³

et al. 2017

JUTI

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“…Such systems release the users from wearing gloves. A survey of existing ArSL recognition systems is in [14].…”

Section: Introductionmentioning

confidence: 99%

Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode

Tubaiz

Shanableh

Assaleh

2015

IEEE Trans. Human-Mach. Syst.

137

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In this paper, we propose a glove-based Arabic sign language recognition system using a novel technique for sequential data classification. We compile a sensor-based dataset of 40 sentences using an 80-word lexicon. In the dataset, hand movements are captured using two DG5-VHand data gloves. Data labeling is performed using a camera to synchronize hand movements with their corresponding sign language words. Low-complexity preprocessing and feature extraction techniques are applied to capture and emphasize the temporal dependence of the data. Subsequently, a Modified k-Nearest Neighbor (MKNN) approach is used for classification. The proposed MKNN makes use of the context of feature vectors for the purpose of accurate classification. The proposed solution achieved a sentence recognition rate of 98.9%. The results are compared against an existing vision-based approach that uses the same set of sentences. The proposed solution is superior in terms of classification rates while eliminating restrictions of vision-based systems.

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Image-Based and Sensor-Based Approaches to Arabic Sign Language Recognition

Cited by 107 publications

References 40 publications

Comparison of Four SVM Classifiers Used with Depth Sensors to Recognize Arabic Sign Language Words

Comparison of Four SVM Classifiers Used with Depth Sensors to Recognize Arabic Sign Language Words

Alphabet Sign Language Recognition Using Leap Motion Technology and Rule Based Backpropagation-Genetic Algorithm Neural Network (Rbbpgann)

Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode

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