Arabic Sign Language Recognition Using Deep Machine Learning
Wael Suliman, Mohamed Deriche, Hamzah Luqman, Mohamed Mohandes
Abstract
In this work, we present an effective method for automatic Arabic Sign Language recognition that uses a Convolutional Neural Network (CNN) for feature extraction and a Long Short-Term Memory (LSTM) for classification. AlexNet, a CNN architecture, is used to extract deep features from the input image while the LSTM is used to preserve the sequential structure of the video frames. The method was tested on a data set consisting of 50 repetitions of 150 signs commonly used in daily activities performed by three signers. The proposed method achieved an overall recognition accuracy of 95.9% for the signer-dependent case, and 43.62% for the more difficult signer-independent case.
Topics & Concepts
Computer scienceConvolutional neural networkArtificial intelligenceFeature extractionSign languageArabicPattern recognition (psychology)Feature (linguistics)Speech recognitionDeep learningSet (abstract data type)Sign (mathematics)Natural language processingMathematical analysisMathematicsLinguisticsProgramming languagePhilosophyHand Gesture Recognition SystemsHearing Impairment and CommunicationGait Recognition and Analysis