Skip to main content
SpringerLink
Log in

RETRACTED ARTICLE: A study on Arabic sign language recognition for differently abled using advanced machine learning classifiers

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

This article was retracted on 15 June 2022

This article has been updated

Abstract

This study is proposed to review the sign language recognition system based on different classifier techniques. Mostly the Neural Network and Deep Learning-based classifiers were utilized to recognize the different sign languages and this survey is proposed to review the best classifier model to represent sign language recognition (SLR). We focused mainly on deep learning techniques and also on Arabic sign language recognition systems. Numerous classifiers like CNN, RNN, MLP, LDA, HMM, ANN, SVM, KNN and more were implemented to the SLR system. Each classifier is reviewed with the recognition accuracy, in which the deep learning-based classifiers executed the optimal recognition result as contrasted to the other types of classifiers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Change history

References

  • Al-Jarrah O, Halawani A (2001) Recognition of gestures in Arabic sign language using neuro-fuzzy systems. Elsevier, Amsterdam, pp 117–138

    MATH  Google Scholar 

  • Alzohairi R, Alghonaim R, Alshehri W, Aloqeely S, Alzaidan M, Bchir O (2018) Image-based Arabic sign language recognition system. Int J Adv Comput Sci Appl 9(3):185–194

    Google Scholar 

  • Ameen S, Vadera S (2017) A convolutional neural network to classify American sign language fingerspelling from depth and color images. Wiley, Hoboken, pp 1–8

    Google Scholar 

  • Arya K, Kudase J (2017) Convolutional neural networks based sign language recognition. Int J Innov Res Comput Commun Eng 5(10):15737–15743

    Google Scholar 

  • Assaleh K, Al-Rousan M (2005) Recognition of Arabic sign language alphabet using polynomial classifiers. EURASIP J Appl Signal Process 2005(13):2136–2145

    MATH  Google Scholar 

  • Bantupalli K, Xie Y (2018) American sign language recognition using deep learning and computer vision. In: IEEE international conference on big data (Big Data), pp 4896–4899

  • Beena MV, Agnisarman Namboodiri MN (2017) Automatic sign language finger spelling using convolution neural network: analysis. Int J Pure Appl Math 117(20):9–15

    Google Scholar 

  • Bhavsar H, Trivedi J (2017) Review on classification methods used in image-based sign language recognition system. Int J Recent Innov Trends Comput Commun 5:949–959

    Google Scholar 

  • Cabalfin EP, Martinez LB, Guevara RCL, Naval PC Jr (2012) Filipino sign language recognition using manifold projection learning. In: TENCON, IEEE

  • Carneiro SB, de Santos EDFM, de Barbosa TMA, Ferreira JO, Soares Alcalá SG, Da Rocha AF (2016) Static gestures recognition for Brazilian sign language with kinect sensor. IEEE, pp 1–3

  • Chansri C, Srinonchat J (2016) Reliability and accuracy of Thai sign language recognition with kinect sensor. IEEE, pp 1–4

  • Choudhury A, Talukdar AK, Sarma KK (2014) A conditional random field-based Indian sign language recognition system under complex background. In: Fourth international conference on communication systems and network technologies. IEEE, pp 900–904

  • Davydov MV, Nikolski IV, Pasichnyk VV (2010) Real-time Ukrainian sign language recognition system. IEEE, pp 875–879

  • Dong C, Leu MC, Yin Z (2015) American sign language alphabet recognition using microsoft kinect. In: IEEE conference on computer vision and pattern recognition workshops (CVPRW), IEEE, pp 44–52

  • ElBadawy M, Elons AS, Shedeed HA, Tolba MF (2017) Arabic sign language recognition with 3D convolution neural network. In: IEEE international conference on intelligent computing and information systems, pp 66–71

  • El-Bendary N, Zawbaa HM, Daoud MS, Hassanien AE, Nakamatsu K (2011) ArSLAT: Arabic sign language alphabets translator. Int J Comput Inf Syst Ind Manag Appl 3(2011):498–506

    Google Scholar 

  • Elmezain M, Al-Hamadi A (2012) LDCRFs-based hand gesture recognition. In: IEEE international conference on systems, man, and cybernetics, pp 2670–2675

  • Elons AS, Ahmed M, Shedid H, Tolba MF (2014) Arabic sign language recognition using leap motion sensor. IEEE, pp 368–373

  • Escobedo E, Camara G (2016) A new approach for dynamic gesture recognition using skeleton trajectory representation and histograms of cumulative magnitudes. In: 29th SIBGRAPI conference on graphics, patterns and images. IEEE, pp 209–216

  • Funasaka M, Ishikawa Y, Takata M, Joe K (2015) Sign language recognition using leap motion controller. In: International conference on parallel and distributed processing techniques and applications, pp 263–269

  • Futane PR, Dharaskar RV (2012) Video gestures identification and recognition using fourier descriptor and general fuzzy minmax neural network for subset of Indian sign language. In: International conference on hybrid intelligent systems, IEEE, pp 525–530

  • Garcia B, Viesca SA (2016) Real-time American sign language recognition with convolutional neural networks, pp 225–232

  • Gawad A, Rabouh A, Samie A, Elmisery FA, Brisha AM, Khalil AH (2018) Arabic sign language recognition using kinect sensor. Res J Appl Sci Eng Technol 15(2):57–67

    Article  Google Scholar 

  • Gebre BG, Wittenburg P, Heskes T (2013) Automatic sign language identification. In: ICIP, IEEE, pp 2626–2630

  • Geng L, Ma X, Xue B, Wu H, Gu J, Li Y (2014) Combining features for Chinese sign language recognition with kinect. In: IEEE international conference on control and automation (ICCA), pp 1393–1398

  • Hartanto R, Susanto A, Santosa PI (2013) Preliminary design of static Indonesian sign language recognition system. IEEE, pp 1–6

  • Hemayed EE, Hassanien AS (2011) Edge-based recognizer for Arabic sign language alphabet (ArS2V—Arabic sign to voice). IEEE, pp 121–127

  • Huang J, Zhou W, Li H, Li W (2015) Sign language recognition using 3d convolutional neural networks. In: IEEE international conference on multimedia and expo (ICME), pp 1–6

  • Islam M, Siddiqua S, Afnan J (2017) Real-time hand gesture recognition using different algorithms based on American sign language. IEEE, pp 1–6

  • Jalal A, Chen R, Moore RK , Mihaylova L (2018) American sign language posture understanding with deep neural networks. In: International conference on information fusion (FUSION). IEEE, pp 573–579

  • Jin CM, Omar Z, Jaward MH (2016) A mobile application of American sign language translation via image processing algorithms. In: IEEE region 10 symposium (TENSYMP). IEEE, pp 104–109

  • Kadry RA, Birry A (2017) ASL recognition using leap motion and hand tracking mechanism. Int J Adv Electron Comput Sci 4(9):97–102

    Google Scholar 

  • Kakoty NM, Sharma MD (2018) Recognition of sign language alphabets and numbers based on hand kinematics using a data glove. In: International conference on robotics and smart manufacturing (RoSMa), procedia computer science, Elsevier, pp 55–62

  • Keskin C, Kıra F, Kara YE, Akarun L (2012) Hand pose estimation and hand shape classification using multi-layered randomized decision forests, ECCV. Springer, Berlin, pp 852–863

    Google Scholar 

  • Kishore PVV, Prasad MVD, Prasad R, Rahul R (2015) 4-Camera model for sign language recognition using elliptical fourier descriptors and ANN. In: International conference on signal processing and communication engineering systems. IEEE, pp 34–38

  • Konwar AS, Borah BS, Tuithung CT (2014) An American sign language detection system using HSV color model and edge detection. In: International conference on communication and signal processing, IEEE, pp 743–747

  • Kumar P, Gauba H, Roy PP, Dogra DP (2016) Coupled HMM-based multi-sensor data fusion for sign language recognition. Pattern Recogn Lett. https://doi.org/10.1016/j.patrec.2016.12.004

    Article  Google Scholar 

  • Kumar P, Gauba H, Roy PP, Dogra DP (2017) A multimodal framework for sensor-based sign language recognition. Neurocomputing. https://doi.org/10.1016/j.neucom.2016.08.132

    Article  Google Scholar 

  • Lahiani H, Elleuch M, Kherallah M (2015) Real-time hand gesture recognition system for android devices. IEEE, pp 591–596

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  • Lee GC, Yeh F-H, Hsiao Y-H (2014) Kinect-based Taiwanese sign-language recognition system. Springer, New York, pp 1–19

    Google Scholar 

  • Liang Z-J, Liao S-B, Hu B-Z (2018) 3D convolutional neural networks for dynamic sign language recognition, computational intelligence, machine learning, and data analytics. Comput J 61:1724–1736

    Article  Google Scholar 

  • Madani H, Nahvi M (2013) Isolated dynamic persian sign language recognition based on camshift algorithm and radon transform. In: First Iranian conference on pattern recognition and image analysis (PRIA). IEEE, pp 1–5

  • Manimurugan S, Narmatha C (2015) Secure and efficient medical image transmission by new tailored visual cryptography scheme with LS compressions. Int J Digit Crime Forensics 7(1):26–50

    Article  Google Scholar 

  • Manimurugan S, Porkumaran K (2011) Fast and efficient secure medical image compression schemes. Eur J Sci Res 56(2):139–150

    Google Scholar 

  • Maraqa M, Al-Zboun F, Dhyabat M, Zitar RA (2012) Recognition of Arabic sign language (ArSL) using recurrent neural networks. J Intell Learn Syst Appl 41–52

  • Miniwatts Marketing Group (2020) Internet World Users by Language. https://www.internetworldstats.com/stats7.htm, updated 11 Nov 2019. Accessed Feb 2020

  • Misra S, Laskar RH (2019) Development of a hierarchical dynamic keyboard character recognition system using trajectory features and scale-invariant holistic modeling of characters. J Ambient Intell Humaniz Comput 10(12):4901–4923

    Article  Google Scholar 

  • Mohana RM, Reddy ARM (2016) Machine learning and data mining techniques for sign language recognition and retrieval system. Int J Eng Comput Res Technol 1(1):1–6

    Google Scholar 

  • Mohandes MA (2013) Recognition of two-handed Arabic signs using the cyber-glove. Springer, New York, pp 669–677

    Google Scholar 

  • Mohandes M, Deriche M, Johar U, Ilyas S (2012) A signer-independent Arabic sign language recognition system using face detection, geometric features, and a Hidden Markov Model. Comput Electr Eng 38:422–433

    Article  Google Scholar 

  • Mohandes M, Aliyu S, Deriche M (2014) Arabic sign language recognition using the leap motion controller. In: IEEE 23rd international symposium on industrial electronics (ISIE), pp 960–965

  • Mustafa M, AbdAlla H, Suleman H (2008) Current approaches in Arabic IR: a survey. In: International conference on Asian digital libraries. Springer, Berlin‏

  • Mustafa M et al (2017) A comparative survey on arabic stemming: approaches and challenges. Intell Inf Manag 9(02):39

    Google Scholar 

  • Nair AV, Bindu V (2013) A review on Indian sign language recognition. Int J Comput Appl 73(22):33–38

    Google Scholar 

  • Narmatha C, Manimegalai P, Manimurugan S (2017) The secure lossless compression scheme for grayscale medical images using PBT and modified steganography. J Adv Res Dyn Control Syst 3(16):96–103 (Special Issues in Recent Trends in Engineering and Managerial Excellence)

    Google Scholar 

  • Nasereddin HH (2017) MMLSL: modelling mobile learning for sign language. Eng Comp Sci Res Rev Appl Sci 9(2):20267–20272

    Google Scholar 

  • Oz C, Leu MC (2011) American sign language word recognition with a sensory glove using artificial neural networks. Eng Appl Artif Intell 24:1204–1213

    Article  Google Scholar 

  • Pansare JR, Ingle M (2016) Vision-based approach for American sign language recognition using edge orientation histogram. In: International conference on image, vision, and computing. IEEE, pp 86–90

  • Paulraj MP, Yaacob S, Desa H, Hema CR (2008) Extraction of head and hand gesture features for recognition of sign language. In: International conference on electronic design, IEEE

  • Porfirio AJ, Wiggers KL, Oliveira LES, Weingaertner D (2013) LIBRAS sign language hand configuration recognition based on 3D meshes. In: IEEE international conference on systems, man, and cybernetics, pp 1588–1593

  • Prasad MVD, Kishore PVV, Kiran Kumar E, Anil Kumar D (2016) Indian sign language recognition system using new fusion based edge operator. J Theor Appl Inf Technol 88(3):574–584

    Google Scholar 

  • Quesada L, López G, Guerrero L (2017) Automatic recognition of the American sign language fingerspelling alphabet to assist people living with speech or hearing impairments. J Ambient Intell Humaniz Comput 8(4):625–635

    Article  Google Scholar 

  • Rahaman MA, Jasim M, Ali MH, Hasanuzzaman M (2014) Real-time computer vision-based Bengali sign language recognition. In: International conference on computer and information technology (ICCIT), pp 194–197

  • Rakun E, Andriani M, Wiprayoga IW, Wiprayoga IW, Tjandra A (2013) Combining depth image and skeleton data from kinect for recognizing words in the sign system for Indonesian Language (SIBI [Sistem Isyarat Bahasa Indonesia]), ICACSIS, pp 387–392

  • Rao GA, Syamala K, Kishore PVV, Sastry ASCS (2018) Deep convolutional neural networks for sign language recognition. In: SPACES-2018. IEEE, pp 194–197

  • Rekha J, Bhattacharya J, Majumder S (2011) Shape, texture and local movement hand gesture features for Indian sign language recognition. IEEE, pp 30–35

  • Rokade RS, Doye DD (2015) Spelled sign word recognition using keyframe. IET Image Process Inst Eng Technol 9(5):381–388

    Article  Google Scholar 

  • Sahoo AK, Mishra GS, Ravulakollu KK (2014) Sign language recognition: state of the art. ARPN J Eng Appl Sci 9(2):116–134

    Google Scholar 

  • Saini R, Kumar P, Roy PP, Dogra DP (2018) A novel framework of continuous human-activity recognition using kinect. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.05.042

    Article  Google Scholar 

  • Shin H-J, Kim WJ, Jang K (2019) Korean sign language recognition based on image and convolution neural network. In: ICIGP, ACM

  • Shukla P, Garg A, Sharma K, Mittal A (2015) A DTW and fourier descriptor based approach for Indian sign language recognition. In: Third international conference on image information processing, IEEE, pp 113–118

  • Sruthi CJ, Lijiya A (2019) Signet: a deep learning based Indian sign language recognition system. In: International conference on communication and signal processing, IEEE, pp 596–600

  • Suharjito HG, Thiracitta N, Nugroho A (2018) Sign language recognition using modified convolutional neural network model. In: INAPR international conference, pp 1–5

  • Suharjito MCA, Wiryana F, Kusuma GP (2018b) A survey of hand gesture recognition methods in sign language recognition. Pertanika J Sci Technol 26:1659–1675

    Google Scholar 

  • Tangsuksant W, Adhan S, Pintavirooj C (2014) American sign language recognition by using 3D geometric invariant feature and ANN classification. In: Biomedical engineering international conference (BMEiCON-2014), IEEE

  • Taskiran M, Killioglu M, Kahraman N (2018) A real-time system for recognition of American sign language by using deep learning. In: International conference on telecommunications and signal processing (TSP), IEEE, pp 258–261

  • Thapa P, Daundkar P, Jadhav M, Khape R (2016) Application for analysing gesture and convert gesture into text and voice. Int J Innov Res Comput Commun Eng 4(10):18999–19002

    Google Scholar 

  • Tofighi G, Venetsanopoulos AN, Raahemifar K, Beheshti S, Mohammadi H (2013) Hand posture recognition using K-NN and support vector machine classifiers evaluated on our proposed hand reader dataset. In: DSP, IEEE

  • Wang H, Chai X, Chen X (2015) Sparse observation (SO) alignment for sign language recognition. Elsevier, Amsterdam, pp 1–12

    Google Scholar 

  • Yadav N, Thepade S, Patil PH (2016) Noval approach of classification based Indian sign language recognition using transform features. In: International conference on information processing, IEEE, pp 64–69

  • Yang X, Chen X, Cao X, Wei S, Zhang X (2016) Chinese Sign language recognition based on an optimized tree-structure framework. In: IEEE journal of biomedical and health informatics, pp 1–11

  • Zhang X, Chen X, Li Y, Lantz V, Wang K, Yang J (2011) A framework for hand gesture recognition based on accelerometer and EMG sensors. IEEE Trans Syst Man Cybern Part A Syst Hum IEEE 41(6):1064–1076

    Article  Google Scholar 

  • Zhang C, Tian Y, Huenerfauth M (2016) Multi-modality American sign language recognition. In: International conference on information processing. IEEE, pp 2881–2885

  • Zheng L, Liang B, Jiang A (2017) Recent advances of deep learning for sign language recognition. IEEE, pp 1–7

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Tabuk, KSA, Tabuk, Saudi Arabia

    Mohammed Mustafa

Authors
  1. Mohammed Mustafa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammed Mustafa.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04142-y"

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mustafa, M. RETRACTED ARTICLE: A study on Arabic sign language recognition for differently abled using advanced machine learning classifiers. J Ambient Intell Human Comput 12, 4101–4115 (2021). https://doi.org/10.1007/s12652-020-01790-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-01790-w

Keywords

Search

Navigation