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                                    Global Journal of Computer Science and Technology: F
                                                                                                                                             
                                    Graphics & vision
                                                                    
                                    Volume 17 I
                                                    ssue 2 Version 1.0 Year 2017
                                                                                        
                                                                                       
                                    Type: Double Blind Peer Reviewed International Research Journal
                                                                                                                       
                                    Publisher: Global Journals Inc. (USA)
                                                                                   
                                    Online ISSN: 0975-4172 & Print ISSN: 0975-4350
                                                                                                    
           
          Towards Arabic Alphabet and Numbers Sign Language 
          Recognition        
                                                                                      By Ahmad Hasasneh & Sameh Taqatqa 
                                                                                                                         Palestine Ahliya University                                                                                       
           
          Abstract- This paper proposes to develop a new Arabic sign language recognition using Restricted 
                        
          Boltzmann Machines and a direct use of tiny images. Restricted Boltzmann Machines are able to 
          code images as a superposition of a limited number of features taken from a larger alphabet. 
          Repeating this process in deep architecture (Deep Belief Networks) leads to an efficient sparse 
          representation of the initial data in the feature space. A complex problem of classification in the input 
          space is thus transformed into an easier one in the feature space. After appropriate coding, a 
          softmax regression in the feature space must be sufficient to recognize a hand sign according to the 
          input image. To our knowledge, this is the first attempt that tiny images feature extraction using deep 
          architecture is a simpler alternative approach for Arabic sign language recognition that deserves to 
          be considered and investigated. 
          Keywords: component; arabic sign language recognition, restricted boltzmann machines, deep belief 
          networks, softmax regression, classification, sparse representation.
                                                                                                             
          GJCST-FClassification: I.5, I.7.5
                                                        
           
          TowardsArabicAlphabetandNumbersSignLanguageRecognition                                                                                                                           
                                                                                   
                                                                                   
                                                                                                                                             
                                                                                   Strictly as per the compliance and regulations of: 
          © 2017. Ahmad Hasasneh & Sameh Taqatqa. This is a research/review paper, distributed under the terms of the Creative Commons 
          Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, 
          distribution, and reproduction inany medium, provided the original work is properly cited. 
                     owards Arabic Alphabet and Numbers Sign 
                  T
                                               Language Recognition 
                                                                            α                       σ
                                                     Ahmad Hasasneh   & Sameh Taqatqa   
                                                            
             Abstra
                   ct- This paper proposes to develop a new Arabic sign        features can be used as a reference to understand the 
                                                    
             language recognition using Restricted Boltzmann Machines          differences among the classes. 
                                                                  
             and a direct use of tiny images. Restricted Boltzmann                      Recognizing and documenting of ArSL have 
                      
             Machines are able to code images as a superposition of a          only been paid attention recently, where few attempts                2017
             limited number of features taken from a larger alphabet.  have investigated and addressed this problem, see for 
             Repeating this process in deep architecture (Deep Belief  example [8]–[11]. The question of ArSL recognition is                        Year
             Networks) leads to an efficient sparse representation of the      therefore a major requirement for the future of ArSL. It                 
             initial data in the feature space. A complex problem of                                                                             15
             classification in the input space is thus transformed into an     facilitates the communication between the deaf and                    
             easier one in the feature space. After appropriate coding, a      normal people by recognizing the alphabet and 
             softmax regression in the feature space must be sufficient to     numbers signs of Arabic sign language to text or 
             recognize a hand sign according to the input image. To our        speech. To achieve that goal, this paper proposes a 
             knowledge, this is the first attempt that tiny images feature     new Arabic sign recognition system based on new 
             extraction using deep architecture is a simpler alternative       machine learning methods and a direct use of tiny 
             approach for Arabic sign language recognition that deserves       images. 
                                
             to be considered and investigated.                                         The rest of the paper is organized as follows. 
             Keywords:      component; arabic sign language 
             recognition, restricted boltzmann machines, deep belief           Section2 presents the current approaches to Arabic 
                                                                               alphabet sign language recognition (ArASLR). Section 3 
             networks, softmax regression, classification, sparse 
                                                                               describes the proposed model for ArASLR. Conclusions 
             representation.                                                   and future works are presented in section 4. 
                               I.  Introduction                                                                                                    )
                                                                                           II. Current Approaches                                  (F
                   ign language continues to be the best method to                                                                                    
                                                                                        Studies in Arabic sign language recognition, 
             Scommunicate between the deaf and hearing 
                   impaired. Hand gestures enable communication                although not as advanced as those devoted to other 
             between deaf people during their daily lives rather than          scripts (e.g. Latin), have recently shown interest [8]– 
             speaking. In our society, Arabic Sign Language (ArSL) is          [11]. We have also seen that current research in ArSLR
                    
             only known for deaf people and specialists, thus the  has only been satisfactory for alphabet recognition with 
             community of deaf people is narrow. To help people  accuracy exceeding 98%. Isolate Arabic word 
             with normal hearing communicate effectively with the  recognition has only been successful with medium-size 
             deaf and the hearing-impaired, numerous systems have              vocabularies (less than 300 signs). On the other hand, 
             been developed for translating diverse sign languages             continuous ArSLR is still in its early stages, with very 
             from around the world. Several review papers have been            restrictive conditions. 
             published that discuss such systems and they can be                        Current approaches on sign language 
             found in [1]–[7].                                                 recognition usually falls into two major approaches. The 
                      Generally, the process of ArSL recognition  first one is sensors based approaches, which employs 
             (ArSLR) can be achieved through two main phases:  sensors attached to the glove. Look-up table software is 
                               
             detection and classification. In stage one, each given            usually provided with the glove to be used for hand 
             image is pre-processed, improved, and then the regions            gesture recognition. Recent sensors based approaches 
             of interest (ROI) is segmented using a segmentation               can be found, for instance, in [11]–[14]. The second 
             algorithm. The output of the segmentation process can             approaches, vision-based analysis, are based on the                  Global Journal of Computer Science and Technology       Volume XVII Issue II Version I 
             thus be used to perform the sign recognition process.             use of video cameras to capture the movement of the 
             Indeed, accuracy and speed of detection play an  hand that is sometimes aided by making the signer wear 
             important role in obtaining accurate and fast recognition         a glove that has painted areas indicating the positions of 
             process. In the recognition stage, a set of features  the fingers and the wrist then use those measurements 
             (patterns) for each segmented hand sign is first  in the recognition process. Image-based techniques 
             extracted and then used to recognize the sign. These              exhibit a number of challenges. These include: lighting 
             Auth                                                              conditions, image background, face and hands 
                or α  σ: Information Technology Department Palestine Ahliya    segmentation, and different types of noise. 
             University Bethlehem, West Bank, Palestine.
             e-mails: ahasasneh@paluniv.edu.ps, sameh@paluniv.edu.ps
                                                                                                                      ©2017   Global Journals Inc.  (US)
                                      wards Arabic Alphabet and Numbers Sign Language Recognition
                                   To
                     Among of image-based approaches, some  focuses on static and simple moving  gestures. The 
            authors [15] introduced a method for automatic  inputs are color images of the gestures. To extract the 
            recognition of Arabic sign language alphabet. For  skin blobs, the YCbCr space is used. The Prewitt edge 
            feature extraction, Hus moments were used followed by          detector is used to extract the hand shape. To convert 
            support vector machines (SVMs) to perform the  the image area into feature vectors, principal component 
            classification process. A correct recognition rate of 87%      analysis (PCA) is used with a K-Nearest Neighbor 
            was achieved. Other authors in [16] developed a neuro-         Algorithm (KNN) in the classification stage. Furthermore, 
            fuzzy system. The proposed system includes five main           the authors in [22] and [23] proposed a pulse-coupled 
            steps: image acquisition, filtering, segmentation, and  neural network (PCNN) ArSLR system able to 
            hand outline detection, followed by feature extraction.        compensate for lighting nonhomogeneity and 
            Bare hands were considered in the experiments,  background brightness. The proposed system showed 
         2017achieving a recognition accuracy of 93.6%. In [17], the       invariance under geometrical transforms, bright 
            authors proposed an adaptive neuro-fuzzy inference  background, and lighting conditions, achieving a 
         Yearsystem for alphabet sign recognition. A colored glove         recognition accuracy of 90%. Moreover, the authors in 
             was used to simplify the segmentation process, and  [24] introduced an Arabic Alphabet and Numbers Sign 
       16 geometric features were extracted from the hand region.          Language Recognition (ArANSLR). The phases of the 
            The recognition rate was improved to 95.5%. In [18], the       proposed algorithm consists of skin detection, 
            authors developed an image-based ArSL system that              background exclusion, face and hands extraction, 
            does not use visual markings. The images of bare  feature extraction, and also classification using Hidden 
            hands are processed to extract a set of features that are      Markov Model (HMM). The proposed algorithm divides 
            translation, rotation, and scaling invariant. A recognition    the rectangle surrounding by the hand shape into zones. 
            accuracy of 97.5% was achieved on a database of 30             The best number of zones is 16 zones. The observation 
            Arabic alphabet signs. In [19], the authors used  of HMM is created by sorting zone numbers in 
            recurrent neural networks for alphabet recognition. A  ascending order depending on the number of white 
            database of 900 samples, covering 30 gestures  pixels in each zone. Experimental results showed that 
            performed by two signers, was used in their  the proposed algorithm achieves 100% recognition rate. 
            experiments. The Elman network achieved an accuracy                     On the other hand, new systems for facilitating 
        )   rate of 89.7%, while a fully recurrent network improved        human machine interaction have been introduced 
         F  the accuracy to 95.1%. The authors extended their work         recently. In particular, the Microsoft Kinect and the leap 
           (by considering the effect of different artificial neural  motion controller (LMC) have attracted special attention. 
            network structures on the recognition accuracy. In  The Kinect system uses an infrared emitter and depth 
            particular, they extracted 30 features from colored  sensors, in addition to a high resolution video camera. 
            gloves and achieved an overall recognition rate of 95%         The LMC uses two infrared cameras and three LEDs to 
            [20].                                                          capture information within its interaction range. 
                     A recent paper reviews the different systems  However, the LMC does not provide images of detected 
            and methods for the automatic recognition of Arabic  objects. The  LMC has recently been used for Arabic 
            sign language can be found in [7]. It highlights the main      alphabet sign recognition with promising results [25]. 
            challenges characterizing Arabic sign language as well                  After presenting the different existing image-
            as potential future research directions. Recent works on       based approaches that have been used to achieve 
            image-based recognition of Arabic sign language  ArASLR, we have noted that these approaches generally 
            alphabet can be found in [9], [10], [21]–[25]. In  include two main phases of coding and classification. 
            particular, Naoum et al. [9] proposes an ArSLR using           We have also seen that most of the coding methods are 
            KNN. To achieve good recognition performance, they  based on hand-crafted feature extractors, which are 
            proposed to combine this algorithm with a glove based          empirical detectors. By contrast, a set of recent 
            analysis technique. The system starts by finding  methods based on deep architectures of neural 
            histograms of the images. Profiles extracted from such         networks give the ability to build it from theoretical 
            histograms are then used as input to a KNN classifier.         considerations. 
         Global Journal of Computer Science and Technology       Volume XVII Issue II Version I Mohandes [10] proposes a more sophisticated ArSLR therefore requires projecting images onto 
            recognition algorithm to achieve high performance of  an appropriate feature space that allows an accurate 
            ArSLR. The first attempt to recognize two-handed signs         and rapid classification. Contrarily to these empirical 
            from the Unified Arabic Sign Language Dictionary using         methods mentioned above, new machine learning 
            the CyberGlove and SVMs to perform the recognition  methods have recently emerged which strongly related 
            process. PCA is used for feature extraction. The authors       to the way natural systems code images [26]. These 
            in [21] proposed an Arabic sign language alphabet  methods are based on the consideration that natural 
            recognition system that converts signs into voice. The         image statistics are not Gaussian as it would be if they 
            technique is much closer to a real-life setup; however,        have had a completely random structure [27]. The auto-
            recognition is not performed in real time. The system          similar structure of natural images allowed the evolution 
            ©20
               1      Journa ls Inc.  (US)
                7   Global
                                       wards Arabic Alphabet and Numbers Sign Language Recognition
                                    To
            to build optimal codes. These codes are made of  DBNs coupled with tiny images can also be successfully 
            statistically independent features and many different  used in the context of ArASLR. 
            methods have been proposed to construct them from                             III.  Proposed Model 
            image datasets. Imposing locality and sparsity 
            constraints in these features is very important. This is                 The methodology of this research mainly 
            probably due to the fact that any simple algorithms  includes four stages (see figure 1) which can be 
            based on such constraints can achieve linear signatures         summarized as follows: 1) data collection and image 
            similar to the notion of receptive field in natural systems.    acquisition, 2)  image pre-processing, 3) feature 
            Recent years have seen an interesting interest in  extraction and finally 4) gesture recognition. 
            computer vision algorithms that rely on local sparse  a)  Description of the Database 
            image representations, especially for the problems of 
            image classification and object recognition [28]–[32].                   The alphabet used for Arabic sign language is 
            Moreover, from a generative point of view, the  displayed in Figure 2, left [38], will be used to                                  2017
            effectiveness of local sparse coding, for instance for  investigate the performance of the proposed model. In 
            image reconstruction [33], is justified by the fact that an     this database, the signer performs each letter                     Year
            natural image can be reconstructed by a smallest  separately. Mostly, letters are represented by a static                              
                                                                                                                                             17
                                                                            posture, and the vocabulary size is limited. In this                
            possible number of features. It has been shown that 
            Independent Component Analysis (ICA) produces  section, several methods for image-based Arabic sign 
            localized features. Besides it is efficient for distributions   language alphabet recognition are discussed.  Even 
            with high kurtosis well representative of natural image         though the Arabic alphabet only consists of 28 letters, 
            statistics dominated by rare events like contours;  Arabic sign language uses 39 signs. The 11 additional 
            however the method is linear and not recursive. These           signs represent basic signs combining two letters. For 
            two limitations are released by DBNs [34] that introduce        example, the two letters “ال” are quite common in Arabic 
            nonlinearities in the coding scheme and exhibit multiple        (similar to the article “the” in English). Therefore, most 
            layers. Each layer is made of a RBM, a simplified version       literature on ArASLR uses these basic 39 signs. 
            of a Boltzmann machine proposed by Smolensky [35]               b)  Image Pre-processing 
            and Hinton [36]. Each RBM is able to build a generative                  The typical input dimension for a DBN is 
            statistical model of its inputs using a relatively fast  approximately 1000 units (e.g. 30x30 pixels). Dealing                     )
            learning algorithm, Contrastive Divergence (CD), first  with smaller patches could make the model unable to                        F
            introduced by Hinton [36]. Another important extract interesting features. Using larger patches can be                               (
            characteristic of the codes used in natural systems, the        extremely time-consuming during feature learning. 
            sparsity of the representation [26], is also achieved in        Additionally the multiplication of the connexion weights 
            DBNs. Moreover, it has been shown that these  acts negatively on the convergence of the CD algorithm. 
            approaches remain robustness to extract local sparse            The question is therefore how could we scale the size of 
            efficient features from tiny images [37]. This model has        realistic images (e.g. 300x300 pixels) to make them 
            been successfully used in [32] to achieve semantic  appropriate for DBNs? 
            place recognition. The hope is to demonstrate that 
                                                                                                                                               Global Journal of Computer Science and Technology       Volume XVII Issue II Version I 
                                                                                                           
                                                             Figure 1: Proposed model
                                                                                                                  ©2017   Global Journals Inc.  (US)
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...Global journal of computer science and technology f graphics vision volume i ssue version year type double blind peer reviewed international research publisher journals inc usa online issn print towards arabic alphabet numbers sign language recognition by ahmad hasasneh sameh taqatqa palestine ahliya university abstract this paper proposes to develop a new using restricted boltzmann machines direct use tiny images are able code as superposition limited number features taken from larger repeating process in deep architecture belief networks leads an efficient sparse representation the initial data feature space complex problem classification input is thus transformed into easier one after appropriate coding softmax regression must be sufficient recognize hand according image our knowledge first attempt that extraction simpler alternative approach for deserves considered investigated keywords component gjcst fclassification towardsarabicalphabetandnumberssignlanguagerecognition strictly ...

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