At the same time, in recent decades, the need for a reliable system that could interact and communicate with people from different nations with different SLs is of great necessity [15]. Educ., vol. As in spoken language, differ- 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. 54 papers with code 7 benchmarks 19 datasets. 1. p. 2000241, 2021. 76, pp. World Federation of the Deaf. 45, no. Sign language recognition - Wikipedia It consists of 201 annotated videos, about 161 videos are used for training and about 40 for testing. Sign Language Recognition for Computer Vision Beginners - Analytics Vidhya JOURNAL OF LA A Comprehensive Review of Sign Language Recognition The following table illustrates this difference. IEEE Computer Society Press. Using camshift for hand tracking, accuracy of 77.75 % was achieved using 91 ASL hand gestures. 10.1109/acpr.2015.7486481.Search in Google Scholar, [76] https://en.wikipedia.org/wiki/Backpropagation.Search in Google Scholar, [77] A. M. Jarman, S. Arshad, N. Alam, and M. J. Islam, An automated bengali sign language recognition system based on fingertip finder algorithm, Int. It consists of 20 lexicons, with 45 repetitions for every word, 20 for training and 18 for testing. 3 Altmetric Abstract This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a prcis of sign linguistics and their impact on the field. Different sensor types attached to hand gloves. 821827, 2019. Sign Language Recognition - Software Engineering Stack Exchange Inf. K. Dixit and A. S. Jalal, Automatic Indian Sign Language recognition system, 2013 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad, 2013, pp. Including ArSL in our future work will be a challenging task. [50] used a face region skin detector which includes eyes and mouth which are non-skin non-smooth regions, which affect and decrease the accuracy. B. Pamahoy, J. R. R. Forteza, and X. J. O. Garcia, Static sign language recognition using deep learning, Int. Ref. 201207, 2016. Among the works developed to address this problem, the majority of them have been based on basically two approaches: contact-based systems, such as sensor gloves; or vision . ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign [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. Sign Language Recognition is a computer vision and natural language processing task that involves automatically recognizing and translating sign language gestures into written or spoken language. O. Kopuklu, A. Gunduz, N. Kose, and G. Rigoll, Real-time hand gesture detection and classification using convolutional neural networks, 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), 2019. 13831388. trends, and barrier concerns to sign language. While communicating with another person using those stored signs, the opposite person can see the signs and its meaning. -G. Kim, An efficient sign language recognition (SLR) system using camshift tracker and hidden markov model (HMM), SN Computer Sci., vol. S. Bilal, R. Akmeliawati, M. J. E. Salami, and A. HumanComputer Interaction Series, Y. Yesilada, S. Harper, eds, London, Springer, 2019.10.1007/978-1-4471-7440-0_3Search in Google Scholar, [2] World Federation of the Deaf. PubMed 5099, pp. N. C. 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Briefly, a dynamic model is implemented on a single frame followed by the gesture of each sequence. 81, pp. Action Recognition mode: User do actions in front of the camera, and then AI Sign Jordan University of Science and Technology, Irbid, 1999. How important are accurate tracking of body parts and its movements? 10.1007/s00500-020-04860-5.Search in Google Scholar, [43] C. D. D. Monteiro, C. M. Mathew, R. Gutierrez-Osuna, F. Shipman, Detecting and identifying sign languages through visual features, 2016 IEEE International Symposium on Multimedia (ISM), 2016. Ref. Sign Language Recognition Application Systems for Deaf - ScienceDirect A. K. Sahoo, Indian sign language recognition using machine learning techniques, Macromol. 201207, 2016. If yes, then the Region-of-Interest (ROI) is detected using hand mask images and segment fingers using defined algorithms. 16. 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It is a very important research area since it can bridge the communication gap between hearing and Deaf people, facilitating the social inclusion of hearing-impaired people. A. Jadhav, G. Tatkar, G. Hanwate, and R. Patwardhan, Sign language recognition, Int. Skin detection is applied on HSV (HUE, and Saturation Values) images and YCbCr. This dataset contains about 855 signs from everyday life domains from different fields such as finance and health. M. W. Kadous, Machine recognition of Auslan signs using PowerGloves: Towards large-lexicon recognition of sign language, Proceedings of the Workshop on the Integration of Gesture in Language and Speech, Wilmington, DE, USA, 1996, pp. 3. p. 672, 2020. 1418, December 2013. 14021407. D. Victor, Real-Time Hand Tracking Using SSD on TensorFlow, GitHub Repository, 2017. 10.1016/j.procs.2015.07.362.Search in Google Scholar, [22] P. Dreuw, D. Rybach, T. Deselaers, M. Zahedi, and H. 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(PDF) Sign Language Recognition - ResearchGate The right hand is detected by applying a forward feed neural network based on VGG-19, and the left image is detected by flipping the image and applying the previous steps again. 2, 79, no. A. Sahoo, G. Mishra, and K. Ravulakollu, Sign language recognition: State of the art, ARPN J. Eng. Best Paper Award, 2007a. Lang. F. Utaminingrum, I. Komang Somawirata, and G. D. Naviri, Alphabet sign language recognition using K-nearest neighbor optimization, JCP, vol. Xidian Univ., vol. 38463849, 2010. From databases like IEEE explore digital library, science direct, springer, web of science, and google scholar, we used the keywords sign language recognition . Deaf. 2, 2005, pp. Table 1 summarizes the results of these comparisons. D. S. Breland, S. B. Skriubakken, A. Dayal, A. Jha, P. K. Yalavarthy, and L. R. Cenkeramaddi, Deep learning-based sign language digits recognition from thermal images with edge computing system, IEEE Sens. 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Vision-based approach: The great development in computer techniques and ML algorithms motivate many researchers to depend on vision-based methodology. As previously illustrated all these kinds of sensors used to measure the bend angles of fingers, the orientation or direction of the rest, abduction, and adduction between fingers. Sign Language (SL) is the main language for handicapped and disabled people. 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. A. 20, no. Mob. HMM is the most widely used technique for speech recognition and SL problems for both vision-based and data-gloves-based approach. Computer Sci., vol. Lang. (PDF) Real Time Sign Language Detection - ResearchGate Hybridization of HMM with CNN provided high accuracy with huge datasets. 1, 2021. Lin, Human computer interaction using face and gesture recognition, 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2013. The effectiveness of the proposed method is validated on a dataset of 720 images with a recognition rate of 96%. HumanComputer Interaction Series, Y. Yesilada, S. Harper, eds, London, Springer, 2019. P. Dreuw, D. Rybach, T. Deselaers, M. Zahedi, and H. Ney, Speech Recognition Techniques for a Sign Language Recognition System, ICSLP, Antwerp, Belgium, August. Using a hand gesture dataset for training and testing, they got an overall accuracy of 97%, Compared two results of using KNN + DTW which produced accuracy of 88.4% but recognition process speed was very high of 9,383 compared to HMM which was 519 to recognize Spanish sign language, Recognized ASL based on HMM using two techniques based on camera mounted on a desk with result of 98% accuracy, and the second used camera stuck to a users cap producing 92% accuracy, Used their own captured dataset from deaf people to represent 20 Arabic words. The overall average accuracy of the system was 93.67%, of which 90.04, 93.44, and 97.52% for ASL alphabets, number recognition, and static word recognition, respectively. Appl., vol. 126, pp. Symp., vol. Accelerometer sensor: It measures 3-axis acceleration caused by gravity and motion; in another word it is used to measure the rate of change of velocity. 20, no. Tests were applied on 6 persons who were signers interpreters and 24 students without any knowledge of using sign language (Figure 11). Mittal et al. RWTH-BOSTON-104: A dataset published by the national center of SL and gesture resources by Boston University. 53 papers with code 7 benchmarks 19 datasets. [48] achieving an average accuracy of 94.32% using convex hull eccentricity, elongatedness, pixel segmentation, and rotation for American number, and alphabets recognition of about 37 signs, whereas ref. SLID will help in breaking down all these barriers for SL across the world. 112119, 2008. 12 (Issue 1), pp. 12551259, 2014. Dewinta and Heryadi [65] classified ASL dataset using KNN classifier, varying the value of K = 3,5,7,9, and 11. 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. 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. 7Hu moments were developed by Mark Hu in 1961. Oliveira et al. Also, we try to compare the accuracies of the dataset when applying different techniques to it [19]. In his system, users must store their signs first in database, after that he can use these signs while communicating with others. 10. First, it considered three dimensions of the layer for temporal dimension. 12. pp. Traditional and deep learning algorithm results applied on the same dataset. Open Computer Science, Vol. Real-time american sign language recognition from video using hidden markov models. Comparison of different feature algorithms. WASL [35] constructed a wide scale ASL dataset from authorized websites such as ASLU and ASL_LEX. 2020, pp. To the best of our knowledge, no prior works have surveyed SLID in previous Literature. 10.1109/iadcc.2013.6514343.Search in Google Scholar, [75] 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. Only arms and hands are considered in the frames. Comput., vol. Also, videos whose background was removed by thresholding were provided. B. Pamahoy, J. R. R. Forteza, and X. J. O. Garcia, Static sign language recognition using deep learning, Int. With K = 3, most researchers get the best accuracy. 2. pp. 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. The preprocessing steps for these datasets, that are prerequisite for all SL aspects, and the required devices will be discussed in Section 3. 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. Using ADAM optimizer he achieved the best result of 99.17% and 98.8% for training and validation, respectively. 16. American sign language recognition and training method with recurrent 10.1109/tmm.2021.3070438.Search in Google Scholar, [81] K. Bantupalli and Y. Xie, American sign language recognition using deep learning and computer vision, 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. Deaf people need to contact and attend online meetings using different platforms such as Zoom, Microsoft Team, and Google Meeting rooms. The phrases have the following format: [adjective1] subject preposition [adjective2] object. AI Sign is able to recognise more than 100+ actions (Sign language in word). This leap device was used to capture 3D information of hands gestures. Flex sensor: It is a very thin and lightweight electric device, used for the measurement of bending or deflection. Table 3 discusses some of the KNN algorithms applied on different datasets. Process, vol. Learn. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC16), 2016, pp. 10.1016/j.icte.2020.08.002.Search in Google Scholar, [88] L. K. Tolentino, R. Serfa Juan, A. Thio-ac, M. Pamahoy, J. Forteza, and X. Garcia, Static sign language recognition using deep learning, Int. Average accuracy is 82.22%, Proposed HMM model to track hand motion in videos. The model is interested in fewer features. Gestuno is considered a pidgin of SLs with limited lexicons. According to Figure 9 it is clear that different algorithms preferred that K-values should not be static to its default value which equals 1, but varying K-values to 1,3,5 or any odd number will result in good results. What is Sign Language? 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). On the other side, for deaf people, many of them thought that ASL is the universal SL. Figure 1 includes a sub-flowchart that illustrates this algorithm. Pattern Anal. 10255, Cham, Springer, 2017. In this section, we shall briefly discuss all these subtasks. Y. R. Wang, W. H. Li and L. Yang, A Novel real time hand detection based on skin color, 17th IEEE International Symposium on Consumer Electronics (ISCE), 2013, pp. [28] built a framework for static and dynamic sign language. Google imposes a graph on 21 points across the fingers, palm and back of the hand, making it easier to understand a hand signal if the hand and arm twist or two fingers touch. 6370, 2019.10.17706/jcp.14.1.63-70Search in Google Scholar, [83] M. M. Kamruzzaman, Arabic sign language recognition and generating Arabic speech using convolutional neural network, Wirel. 2. pp. This article covers the first two tasks: SLR and SLID. Also, the proposed model accuracy exceeds other classifiers such as KNN (95.95%), SVM (97.9%), and ANN (98%). This shortage was due to the need for experts who can explain and illustrate many different SLs to researchers. Appl. Unfortunately, deaf people do not have a written form and have a huge lack of electronic resources. Sign language is regarded as a separate language from other spoken languages [1]. Y.-J. First, the author used the algorithm of connected components analysis to select and segment hands from the image dataset using masks and filters, finger cropping, and segmentation. Sign language is set to become official in South Africa - how this will help education in schools for the Deaf . [39] created an ISL dataset recorded by six participants. C. Oz and M. C. Leu, American sign language word recognition with a sensory glove using artifcial neural networks, Eng. doi: 10.1109/CVPR.2017.494. 2, 2005, pp. 30, no. Comput., vol. Binyam Gebrekidan Gebre [11] proposed a method that gathers two methods of Stokoes and H-M model as they assumed that features extracted from frames are independent of each other, But Gebre assumes that signs features will be extracted from two frames. 4, October 2018, Pages 470477. 116134, 2014.Search in Google Scholar, [15] A. Karpov, I. Kipyatkova, and M. elezn, Automatic technologies for processing spoken sign languages, Proc. Ref. AI Sign : Sign Language on the App Store Figure 1 shows the sequence of different preprocessing steps that are almost required for different SL models. How to test and train the model? [65] achieved results of 28.6% accuracy when applying KNN with PCA for dimensionality reduction. SLID has many subtasks starting from image preprocessing, segmentation, feature extraction, and image classification. Appl. For a modeling step, it used a random forest algorithm which generates many decision tree classifiers and aggregates their results. 9, pp. They must be unique, normalized, and preprocessed. Existing Methods of Sign Language Recognition Static Hand Gesture Detection Model Result Observation New Result What is Transfer Learning? Yuxiao, L. Zhao, X. Peng, J. Yuan, and D. Metaxas, Construct Dynamic Graphs for Hand Gesture Recognition Via Spatial-temporal Attention, UK, 2019, pp. 20, no. Unfortunately, every research has its own limitations and are still unable to be used commercially. 38463849, 2010, 10.1109/ICPR.2010.937.Search in Google Scholar, [21] 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. The recognition of SASL as an official language is a big step in the right direction. 84, no. It was recorded using the state-of-the-art Microsoft Kinect v2 sensor. of the IEEE International Conference on Instrumentation and Measurement Technology 2007, Warsaw, 2007, pp. A. Kumar and S. Malhotra, Real-Time Human Skin Color Detection Algorithm Using Skin Color Map, 2015. Extracted features include high performance, flexibility, and stability. Dataset was performed by 119 signers, producing only one video for every sign. D. Cokely, Charlotte Baker-Shenk, American Sign Language, Washington, Gallaudet University Press, 1981. Process, vol. 110, 2015.Search in Google Scholar, [78] P. P. Roy, P. Kumar, and B. A. M. Jarman, S. Arshad, N. Alam, and M. J. Islam, An automated bengali sign language recognition system based on fingertip finder algorithm, Int. RWTH German fingerspelling [21]: A German SL dataset is collected from 20 participants, producing about 1,400 image sequences. Four cameras were used to capture signs, three of them are white/black cameras and one is a color camera. 10.1007/978-3-319-58838-4_35.Search in Google Scholar, [90] A. Elboushaki, R. Hannane, A. Karim, and L. Koutti, MultiD-CNN: a multi-dimensional feature learning approach based on deep convolutional networks for gesture recognition in RGB-D image sequences, Expert. 14591469. Non-skin images are rejected, while other images continue the processing by applying image morphology (erosion and dilation) for noise reduction. 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. The upcoming sections are arranged as follows: Datasets of different SLs are described in Section 2.
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