Each sign was repeated 50 times by each signer, using Microsoft Kinect V2 for sign recording. How important are accurate tracking of body parts and its movements? Ref. 1523915248, 2020. Open Computer Science, Vol. N. Ibrahim, H. Zayed, and M. Selim, An automatic arabic sign language recognition system (ArSLRS), J. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC16), 2016, pp. KNN classification comparison on different datasets. 6067, 2015, Sign language identification and recognition: A comparative study, Special Issue on Programming Models and Algorithms for Big Data, Downloaded on 28.6.2023 from https://www.degruyter.com/document/doi/10.1515/comp-2022-0240/html, Classical and Ancient Near Eastern Studies, Library and Information Science, Book Studies, An ROI-based robust video steganography technique using SVD in wavelet domain, SIKM a smart cryptographic key management framework, Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR model, 3D chaotic map-cosine transformation based approach to video encryption and decryption, Security and privacy issues in federated healthcareAn overview, Designing of fault-tolerant computer system structures using residue number systems, A method for detecting objects in dense scenes, An effective integrated machine learning approach for detecting diabetic retinopathy, Greatest-common-divisor dependency of juggling sequence rotation efficient performance, Construction of a gas condensate field development model, A novel similarity measure of link prediction in bipartite social networks based on neighborhood structure, Rough set-based entropy measure with weighted density outlier detection method, Word2Vec: Optimal hyperparameters and their impact on natural language processing downstream tasks, Post-quantum cryptography-driven security framework for cloud computing, BiSHM: Evidence detection and preservation model for cloud forensics, Two hide-search games with rapid strategies for multiple parallel searches, A student-based central exam scheduling model using A* algorithm, Deep learning-based ensemble model for brain tumor segmentation using multi-parametric MR scans, Design of a web laboratory interface for ECG signal analysis using MATLAB builder NE, An alternative C++-based HPC system for Hadoop MapReduce, A new watermarking scheme for digital videos using DCT, Rainfall prediction system for Bangladesh using long short-term memory, A flexible framework for requirement management (FFRM) from software architecture toward distributed agile framework, Wormhole attack detection techniques in ad-hoc network: A systematic review, Research on the structure of smart medical industry based on the background of the internet of things, Mass data processing and multidimensional database management based on deep learning, Research on the virtual simulation experiment evaluation model of e-commerce logistics smart warehousing based on multidimensional weighting, Cross-modal biometric fusion intelligent traffic recognition system combined with real-time data operation, Big data network security defense mode of deep learning algorithm, A study on the big data scientific research model and the key mechanism based on blockchain, Study on the random walk classification algorithm of polyant colony, Privacy protection methods of location services in big data, Data sharing platform and security mechanism based on cloud computing under the Internet of Things, Multisource data acquisition based on single-chip microcomputer and sensor technology, Microsoft Kinect, 2 video cameras and 3 webcams, Raspberry PI and Omron D6T thermal camera, Template matching technique was applied with best recognition of hand's gestures, then used KNN for time reduction, KNN classifier used to detect and recognize ASL, giving promising accuracy with, Applied KNN with SMART technique used to improve weights and get best accuracy, Recognize Arabic sign language based on two DG5-VHand data gloves electronic device. Most widely used electronic devices for hand gesture recognition. Previous gloves required an accompanying camera to register the gesture but does not work well in lightning conditions. Non-skin images are rejected, while other images continue the processing by applying image morphology (erosion and dilation) for noise reduction. M. Abdel-Fattah, Arabic Sign Language: A Perspective, J. 10.1109/CSPA.2010.5545253.Search in Google Scholar, [54] T. Simon, H. Joo, I. Matthews, and Y. Sheikh, Hand Keypoint Detection in Single Images Using Multiview Bootstrapping 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 110, 2015.Search in Google Scholar, [78] P. P. Roy, P. Kumar, and B. SMILE [30]: It prepared an assessment system for lexical signs of Swiss German Sign Language that relies on SLR. [48] applied five types of feature extraction including fingertip finder, elongatedness, eccentricity, pixel segmentation, and rotation. However, most communication technologies operate in spoken and written languages, creating inequities in access. 8, pp. Other large US. SMART was used to optimize and enhance accuracy of KNN classifier. Sci. Ref. Ref. O.Al-Jarrah and A. Halawani, Recognition of gestures in Arabic sign language using neuro-fuzzy systems, Artif. 10.1016/j.procs.2016.04.050.Search in Google Scholar, [16] F. Chou and Y. Su, An encoding and identification approach for the static sign language recognition, 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Kachsiung, 2012, pp. Appl., vol. 21, no. 14, no. 226 signs were captured by 43 different signers, producing 38,336 isolated sign videos. Y.-J. On the other hand, few researchers have focused on SLID [11]. This article covers the first two tasks: SLR and SLID. So, we need to identify and globalize a unique SL as excluding deaf people and discarding their attendance will affect the whole work progress and damage their psyche which emphasizes the principle of nothing about us without us. Also, SL occupies a big space of all daily life activities such as TV sign translators, local conferences sign translators, and international sign translators which is a big issue to translate all conferences points to all deaf people from different nations, as every deaf person requires a translator of their own SL to translate and communicate with him. Sultan, A., Makram, W., Kayed, M. and Ali, A. 159160, 1999. Ref. This dataset contains about 855 signs from everyday life domains from different fields such as finance and health. 1, 2021. We are pleased to showcase the creation and implementation of sign language recognition model based on a Convolutional Neural Network (CNN).We utilized a Pre-Trained SSD Mobile net V2. Tu, C.-C. Kao, and H.-Y. D. Bragg, O. Koller, M. Bellard, L. Berke, P. Boudreault, A. Braffort, et al., Sign language recognition, generation, and translation: an interdisciplinary perspective, The 21st International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 19), New York, NY, USA, Association for Computing Machinery, 2019, pp. Many datasets were used in SL recognition, some of these datasets are based on the approaches of vision and some are based on the approach of soft computing like ANN, Fuzzy Logic, Genetic Algorithms, and others like Principal Component Analysis (PCA) and deep learning like Convolutional Neural Network (CNN). U. Shrawankar and S. Dixit, Framing Sentences from Sign Language Symbols using NLP, In IEEE conference, 2016, pp. F. Raheem and A. D. Aryanie and Y. Heryadi, American sign language-based finger-spelling recognition using k-Nearest Neighbors classifier. 2015 3rd International Conference on Information and Communication Technology (ICoICT), 2015, pp. Third, there are a fewer number of specialized people who are fluent or professional in practicing this language. F. Chou and Y. Su, An encoding and identification approach for the static sign language recognition, 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Kachsiung, 2012, pp. Jadhav et al. 10.3390/s20143879.Search in Google Scholar 2. pp. Comput. R. Akmeliawati, Real-time Malaysian sign language translation using colour segmentation and neural network, Proc. 8, 2017. Commun. 324329. 24, pp. The dataset contains 35 sign words, each word was repeated at least 15 times by each participant, so the size of the dataset is 3,150 (35 15 6). 150, p. 113336, 2020a. 49134921.10.1109/ICCV.2017.525Search in Google Scholar, [73] D. Victor, Real-Time Hand Tracking Using SSD on TensorFlow, GitHub Repository, 2017.Search in Google Scholar, [74] K. Dixit and A. S. Jalal, Automatic Indian sign language recognition system, 2013 3rd IEEE International Advance Computing Conference (IACC), 2013. The second camera was mounted to a cap of the user, producing 98% accuracy. They may use sign language as their primary way of communication. Intell., vol. Different sign languages, modalities, and datasets in sign language have been discussed and presented in tabular form to understand better. T. Johnston and A. Schembri, Australian Sign Language (Auslan): An Introduction to Sign Language Linguistics, Cambridge, UK, Cambridge University Press, 2007. A. Youssif, A. Aboutabl, and H. Ali, Arabic sign language (ArSL) recognition system using HMM, Int. [89] applied CNN algorithm on Bhutanese Sign Language digits recognition, collected dataset of 20,000 images of digits [09] from 21 students, each student was asked to capture 10 images per class. Each country has its own SL that is different from other countries. Pattern Anal. Z. Shukor, M. F. Miskon, M. H. Jamaluddin, F. Bin Ali, M. F. Asyraf, and M. B. Bin Bahar., A new data glove approach for malaysian sign language detection, Procedia Computer Science, vol. First, it considered three dimensions of the layer for temporal dimension. Skin-detection: It is the process of separating the skin color from the non-skin color. P. Dreuw, D. Rybach, T. Deselaers, M. Zahedi, and H. Ney, Speech Recognition Techniques for a Sign Language Recognition System, ICSLP, Antwerp, Belgium, August. 10. J., vol. What is Sign Language? A. Karpov, I. Kipyatkova, and M. elezn, Automatic technologies for processing spoken sign languages, Proc. K. B. Shaik, P. Ganesan, V. Kalist, B. S. Sathish, and J. M. M. Jenitha, Comparative study of skin color detection and segmentation in HSV and YCbCr color space, Proc. 18711881, 2020. Appl. This survey will also be helpful in our next research which will be about SLID, which requires deep understanding of more trending techniques and procedures used in SLR and SLID. Signer dependent is the main core of any SLR system, as the signer performs both training and testing phases. 2. pp. in terms of voltage values to the system, but on the other side, vision-based systems require to apply tracking and feature extraction algorithms. Although, Gestuno cannot be considered as a language due to several reasons. 10.1007/s42979-021-00485-z.Search in Google Scholar, [79] S. Ghanbari Azar and H. Seyedarabi, Trajectory-based recognition of dynamic persian sign language using hidden Markov Model, arXiv e-prints, p. arXiv-1912, 2019.Search in Google Scholar, [80] N. M. Adaloglou, T. Chatzis, I. Papastratis, A. Stergioulas, G. T. Papadopoulos, V. Zacharopoulou, and P. Daras, A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition, IEEE Transactions on Multimedia, p. 1, 2021. 141, pp. He Proposed an ideal SLID, the system subcomponents are: (1) skin detection, (2) feature extraction, (3) modeling, and (4) identification. Deaf people need to contact and attend online meetings using different platforms such as Zoom, Microsoft Team, and Google Meeting rooms. Existing methods of event-based SLR rely on a uniform sampling strategy, which may result in . 10.1155/2020/3685614.Search in Google Scholar, [84] M. Varsha and C. S. Nair, Indian sign language gesture recognition using deep convolutional neural network, 2021 8th International Conference on Smart Computing and Communications (ICSCC), IEEE, 2021.10.1109/ICSCC51209.2021.9528246Search in Google Scholar, [85] M. Z. Islam, M. S. Hossain, R. ul Islam, and K. Andersson, Static hand gesture recognition using convolutional neural network with data augmentation, 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Spokane, WA, USA, 2019, pp. Umang [68] applied KNN and PNN as a classification technique to recognize ISL alphabets. The latter task is targeted to identify the signer language, while the former is aimed to translate the signer conversation into tokens (signs). It discusses both the vision-based and the data-gloves-based approaches, aiming to analyze and focus on main methods used in vision-based approaches such as hybrid methods and deep learning algorithms. 201207, 2016. 397, no. Also, trying to wear-off any gloves or any electric based systems will give user more comfort while communicating with others. CNN [76] Kang et al. A. Shafie, Dynamic approach for real-time skin detection, J. Real-Time Image Proc., vol. SLR basically depends on what is the translation of any hand gesture and posture included in SL, and continues/deals from sign gesture until the step of text generation to the ordinary people to understand deaf people. 2. pp. Furthermore, this article compares the different machine and deep learning models applied on different datasets, identifies best deep learning parameters such as, neural network, activation function, number of Epochs, best optimization functions, and so on, and highlights the main state of the art contributions in SLID. Ref. Provided with proper lighting condition and a uniform background, the system acquired an average testing accuracy of 93.67%, of which 90.04% was attributed to ASL alphabet recognition, 93.44% for number recognition and 97.52% for static word recognition, thus surpassing that of other related studies. In his system, users must store their signs first in database, after that he can use these signs while communicating with others. Ref. 21, no. 81, pp. Ref. 109115, no. J. After retraining the model using the inception model, the extracted features were passed to RNN using LSTM Model. PubMed Central, [69] A. K. Sahoo, Indian sign language recognition using machine learning techniques, Macromol. . According to this previous issue, we need to get rid of any obstacles (gloves, sensors, and leap devices) or any electronic device that may restrict user interaction with the system. J. Adv. A. Jadhav, G. Tatkar, G. Hanwate, and R. Patwardhan, Sign language recognition, Int. Image resize: It is the process of resizing images by either expanding or decreasing image size. Comput., vol. A. Kindirolu, S. Karabkl, M. Kelepir, A. S. Ozsoy, and L. Akarun, BosphorusSign: a Turkish sign language recognition corpus in health and finance domains. 14591469. SLID has many subtasks starting from image preprocessing, segmentation, feature extraction, and image classification. About 95% F1 score of accuracy was achieved. 1. p. 2000241, 2021. About 80-words lexicon were used to build up 40 sentences, Used MATLAB functions to convert captured hand gestures into text and speech based on classification algorithms (PNN and KNN) to recognize ISL alphabets, Proposed a wearable electronic device to recognize 10 gestures of Italian sign language. About 100 native signers of different ages participated in collecting and recording signs for about 72h. It provided annotation or translation of some of these signs. Tilt sensor: It is a device that produces an electrical signal that varies with an angular movement, used to measure slope and tilt with a limited range of motion. 108125, 2015. [78] also used FFANN for Bengali alphabet with 46 signs achieving an accuracy of 88.69% for testing result depending on Fingertip finder algorithm with multilayered feedforward, back propagation training. 7Hu moments were developed by Mark Hu in 1961. J. Electron. Gestuno is considered a pidgin of SLs with limited lexicons. N. El-Bendary, H. M. Zawbaa, M. S. Daoud, A. E. Hassanien, K. Nakamatsu, ArSLAT: Arabic Sign Language Alphabets Translator, 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM), Krackow, 2010, pp. Comput., vol. A. Kindirolu, S. Karabkl, M. Kelepir, A. S. Ozsoy, and L. Akarun, BosphorusSign: a Turkish sign language recognition corpus in health and finance domains. COVID-19 Coronavirus is a global pandemic that forced a huge percentage of employees to work and contact remotely. Performance measure before and after applying data augmentation. 20, no. Univ. 12. pp. 10.1109/etcm.2018.8580268.Search in Google Scholar, [63] L. Chen, J. Fu, Y. Wu, H. Li, and B. Zheng, Hand gesture recognition using compact CNN via surface electromyography signals, Sensors, vol. M. Z. Islam, M. S. Hossain, R. ul Islam, and K. Andersson, Static hand gesture recognition using convolutional neural network with data augmentation, 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Spokane, WA, USA, 2019, pp. Parcheta and Martnez-Hinarejos [71] used an optimized sensor called leap motion that we presented previously. 6370, 2019. Most researchers prefer vision-based method because of its frameworks adaptability, the involvement of facial expression, body movements, and lips perusing. Section 5 comprehensively compares the results and the main contributions of these addressed models. This article highlights two main SL processing tasks: Sign Language Recognition (SLR) and Sign Language Identification (SLID). Action Recognition mode: User do actions in front of the camera, and then AI Sign RKS-PERSIANSIGN [33] include a large dataset of 10 contributors with different backgrounds to produce 10,000 videos of Persian sign language (PSL), containing 100 videos for each PSL word, using the most commonly used words in daily communication of people. For example, expressing the sign CUP with different mouth positions may indicate cup size, also body movements which may be included while expressing any SL provides different meanings. Intellig., vol. Building an interactive unified recognizer system is a challenge [11] as there are many words/expressions with the same sign in different languages, other words/expressions with different signs in the different languages, and other words/expressions could be expressed using the hands beside the movements of the eyebrows, mouth, head, shoulders, and eye gaze. 10.1109/IAdCC.2013.6514343.Search in Google Scholar, [24] I. Images were scaled to 64 64. A. I. Sidig, H. Luqman, S. Mahmoud, and M. Mohandes, KArSL: Arabic sign language database, ACM Trans. This shortage was due to the need for experts who can explain and illustrate many different SLs to researchers. Lin, Human computer interaction using face and gesture recognition, 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2013. 14, no. https://bmvc2019.org/wp-content/uploads/papers/0281-paper.pdf. This discarding is a challenge in adapting the system to accept another signer. RGB is a widely used color mode, but it is not preferred in skin detection because of its chrominance and luminance and its non-uniform characteristics. 14, pp. Comput., vol. Most proposed systems achieved promising results and indicated significant improvements in SL recognition accuracy. HMM was used as a classifier with Gaussian density function as for observations. Sign language is an essential tool to bridge the communication gap between normal and hearing-impaired people. In this section, we discuss many datasets that had been used in different SL aspects such as skin and body detection, image segmentation, feature extraction, gesture recognition, and sign identification for more advanced approaches. 84, no. Sci., vol. Sci., Vol. 10.1007/978-3-319-58838-4_46.Search in Google Scholar, [71] T. Starner, J. Weaver, and A. Pentland, Real-time American sign language recognition using desk and wearable computer-based video, IEEE Trans. Sign Language Recognition (SLR) is a fascinating research area and a crucial task concerning computer vision and pattern recognition. 113. Xidian Univ., vol. It is defined as a mode of interaction for the hard of hearing people through a collection of hand gestures, postures, movements, and facial expressions or movements which correspond to letters and words in our real life. Computer Appl., vol. N. M. Adaloglou, T. Chatzis, I. Papastratis, A. Stergioulas, G. T. Papadopoulos, V. Zacharopoulou, and P. Daras, A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition, IEEE Transactions on Multimedia, p. 1, 2021. 7, no. The need for an organized and unified SL was first discussed in World Sign Congress in 1951. U. Patel and A. G. Ambekar, "Moment Based Sign Language Recognition for Indian Languages," 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), 2017, pp. SL is the bridge for communication between deaf and normal people. Sign Language Recognition: A Deep Survey Razieh Rastgoo, . Each model usually starts with a signers image, applying color space conversion. Sign languages are expressed through manual articulation in combination with non-manual markers. Different sign languages, modalities, and datasets in sign language have been discussed and presented in tabular form to understand better. Each image is validated by checking whether it has a hand or not. The preprocessing steps for these datasets, that are prerequisite for all SL aspects, and the required devices will be discussed in Section 3. 76, pp. In some jurisdictions (countries, states, provinces or regions), a signed language is recognised as an official language; in others, it has a protected status in certain areas (such as education). 10.3390/s20030672.Search in Google Scholar Z. Parcheta and C.-D. Martnez-Hinarejos, Sign language gesture recognition using HMM, in Pattern Recognition and Image Analysis. 119, 2021. Yuxiao, L. Zhao, X. Peng, J. Yuan, and D. Metaxas, Construct Dynamic Graphs for Hand Gesture Recognition Via Spatial-temporal Attention, UK, 2019, pp. A clear reason for depending on deep learning is that it had repeatedly demonstrated high quality results on a wide variety of tasks, especially those with big datasets. F. Utaminingrum, I. Komang Somawirata, and G. D. Naviri, Alphabet sign language recognition using K-nearest neighbor optimization, JCP, vol. Four cameras were used to capture signs, three of them are white/black cameras and one is a color camera. Each input frame is convolved with more than 32 filters to cover the networks scope which is narrower at the beginning. 4148, 2015. Please login or register with De Gruyter to order this product. J., vol. It extends over the temporal dimension; this is useful in sign language recognition because it helps to model the local variations that describe the trajectory of gesture during its movement. You're going to have to do a lot of data cleaning/filtering before it gets to the HMM, however. Dynamic Sign Language Model: It is concerned with two key-points. S. Wilcox and J. Peyton, American Sign Language as a foreign language, CAL. 419426, 2017. Finally, the dataset was divided into two groups for training and testing. Z. Onno Crasborn and J. Ros, Corpus-NGT. Virtual Button approach [57]: Depends on a virtual button generated by the system and receives hands motion and gesture by holding and discharging individually. Inform., vol. 30, no. Learn. [88] implemented training and testing using CNN by Keras and TensorFlow using SGD algorithm as its optimizer, having a learning rate of 0.01. Motion (proximity) sensor: It is an electrical device which utilizes a sensor to capture motion, or it is used to detect the presence of objects without any physical contact. Process, vol. Accuracy for model 1 is 99.3% as it was able to recognize all 24 letters, but the accuracy of model 2 was 83.33% as it recognizes only 20 letters of all the 24 letters. According to higher results in SLR on different SLs, a new task of SLID arises to achieve more stability and facility in deaf and ordinary people communication. [28] built a framework for static and dynamic sign language. Comput., vol. D. Victor, Real-Time Hand Tracking Using SSD on TensorFlow, GitHub Repository, 2017. Below is a short description of most used devices for SLR. This paper presents an isolated sign language recognition system that comprises of two main phases: hand tracking and hand representation. Hidden Markov Model (HMM): Based on our review, HMM was one of the strongly recommended approaches for SL problems. 10255, Cham: Springer, pp. arXiv preprint arXiv:1812.01053, arXiv 2018, arXiv:1812.01053. Sign languages vary from one country to another and each has a specific vocabulary. 9, pp. Tu, C.-C. Kao, and H.-Y. 2, 2005, pp. Spoken languages like English are the most used language between all countries and many people thought it is a globally spoken language. Dig., pp. Comput., vol. https://bmvc2019.org/wp-content/uploads/papers/0281-paper.pdf.Search in Google Scholar, [93] A. 16. pp. In the last decade, researchers depended on electronic devices to detect and recognize hand position and its gestures, because of many reasons [55]. Human-Mach. 1. pp. Ref. 590595.10.1109/CISIM.2010.5643519Search in Google Scholar, [11] B. G. Gebre, P. Wittenburg, and T. Heskes, Automatic sign language identification, 2013 IEEE International Conference on Image Processing, Melbourne, VIC, 2013, pp. 14591469.10.1109/WACV45572.2020.9093512Search in Google Scholar, [36] O. M. Sincan and H. Y. Keles, AUTSL: A large-scale multi-modal Turkish sign language dataset and baseline methods, IEEE Access, vol. CNN algorithms accuracy was 96.2% which is higher than SVM classification algorithm applied by the author to achieve an accuracy of 93.5%. From databases like IEEE explore digital library, science direct, springer, web of science, and google scholar, we used the keywords sign language recognition . As in spoken language, differ- 2020, pp. of the IEEE International Conference on Instrumentation and Measurement Technology 2007, Warsaw, 2007, pp. B. Kang, S. Tripathi, and T. Nguyen, Real-time sign language fingerspelling recognition using convolutional neural networks from depth map, 3rd IAPR Asian Conference on Pattern Recognition, Kuala Lumpur, Malaysia, 2015. https://en.wikipedia.org/wiki/Backpropagation. It's an opportunity for the almost 4 million Deaf people in South Africa (of whom 600,000 are SASL users) to properly access their human rights in a language they understand. B. Chaudhuri, A modified LSTM model for continuous sign language recognition using leap motion, IEEE Sens. 13711375, 1998. 2, 2005, pp. It compared between vison-based approach and glove-based approach, showed the advantages and the disadvantages of both, illustrated the difference between signer dependent and signer independent, and addressed the basic preprocessing steps such as skin detector, image segmentation, hand tracking, feature extraction, and hands gesture classification. 9, pp. Another disadvantage and disability are wearing gloves because users must interact with systems using gloves. 1. pp. 20, no. Comparison of different machine learning algorithms based on different datasets, Comparison of deep learning of different sign language datasets focusing on technical parameters such as activation and optimization function, learning rate, and so on. These sensors give an advantage over vison-based systems. [1] This is an essential problem to solve especially in the digital world to bridge the communication gap that is faced by people with hearing impairments. A. M. Jarman, S. Arshad, N. Alam, and M. J. Islam, An automated bengali sign language recognition system based on fingertip finder algorithm, Int. Ref. 112119, 2008.10.1007/978-3-540-69905-7_13Search in Google Scholar, [51] S. Bilal, R. Akmeliawati, M. J. E. Salami, and A. O. Koller, S. Zargaran, H. Ney, and R. Bowden, Deep sign: enabling robust statistical continuous sign language recognition via hybrid CNN-HMMs, Int. 139, p. 112829, 2019. 15. 1. pp. Sony digital camera with resolution up to 16.1MP is used. 48964899. Bold indicates highest results of applying different CNN models on various SL datasets. 7276.
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