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Real-Time Hand Detection and Recognition For Sign Language - SSRN Introduction According to the World Federation of the Deaf, there are over 300 sign languages around the world that 70 million deaf people are using them ( Murray, 2018 ). used CNN, specifically caffe implementation network (CaffeNet), consisting of 5 convolution layers, 3 max-pooling layers, and 3 fully connected layers. Syst. Most researchers prefer vision-based method because of its frameworks adaptability, the involvement of facial expression, body movements, and lips perusing. ANFIS: Adaptive Neuro-Fuzzy Inference system, MSL: Malaysian Sign Language, IBL: instance-based learning, DTL: decision-tree learning, ISL: Indian Sign Language. Their values are invariant because of the change in size, rotation, and translation. Ref. He used an interpolation algorithm for shadow detection from the images and fills it with continuous dots using FillHoles method. 16. pp. PDF JOURNAL OF LA A Comprehensive Review of Sign Language Recognition PubMed This approach is not effective for recognizing SL because every sign language required utilization of all hands fingers and it also cannot be practical for real life communication. IbPRIA 2017. 10, no. 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. Most recent SLR works [17-19,22,23,25,33, 35,39,67,70] adopt CNN-based architectures, e.g., I3D [4] and R3D [44], to extract vision features from RGB videos. Oliveira et al. 70567063, 2019. 533536. 344346.Search in Google Scholar, [8] D. Cokely, Charlotte Baker-Shenk, American Sign Language, Washington, Gallaudet University Press, 1981.Search in Google Scholar, [9] U. Shrawankar and S. Dixit, Framing Sentences from Sign Language Symbols using NLP, In IEEE conference, 2016, pp. %Skeleton Aware Multi-modal Sign Language Recognition In this work, we focused on investigating two questions: how fine-tuning on datasets from other sign languages helps improve sign recognition . 2. pp. Xidian Univ., vol. Sci., 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. It has about 496 samples in health domain, about 171 samples in finance domain, and the remaining signs (about 181) are commonly used signs in everyday life. 4148, 2015. 13831388. 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. Multiple Dataset [25]: Collected two datasets of ArSL, consisting of 40 phrases with 80-word lexicon, each phrase was repeated 10 times, using DG5-Vhand data glove with five sensors on each finger with an embedded accelerometer. Xidian Univ., vol. He applied one of the two available types of HMM which is discrete and continuous. Four cameras were used to capture signs, three of them are white/black cameras and one is a color camera. PDF Vision-based Sign Language Recognition System - CORE Although a government may stipulate in its constitution (or laws) that a "signed language . This number has increased to 466 million by 2020, and it is estimated that by 2050 over 900 million people will have hearing loss disability. 133, pp. The hand region is detected using skin-detection from the original image using some defined masks and filters as shown in Figure 2. A. I. Sidig, H. Luqman, S. Mahmoud, and M. Mohandes, KArSL: Arabic sign language database, ACM Trans. However, the diversity of over 7000 present-day sign languages with variability in motion position, hand shape, and position of body parts making automatic sign language recognition (ASLR) a complex system. Intell., vol. 10.1016/j.jksuci.2017.09.007.Search in Google Scholar, [53] M. P. Paulraj, S. Yaacob, Z. Azalan, M. Shuhanaz, and R. Palaniappan, A Phoneme-based Sign Language Recognition System Using Skin Color Segmentation, 2010, pp. doi: 10.1109/CVPR.2017.494.10.1109/CVPR.2017.494Search in Google Scholar, [55] R. Akmeliawati, Real-time Malaysian sign language translation using colour segmentation and neural network, Proc. Most proposed systems achieved promising results and indicated significant improvements in SL recognition accuracy. 48964899. Virtual Button approach [57]: Depends on a virtual button generated by the system and receives hands motion and gesture by holding and discharging individually. 20, no. 181340181355, 2020.10.1109/ACCESS.2020.3028072Search in Google Scholar, [37] A. 10.37896/jxu14.4/212.Search in Google Scholar, [45] A. Kumar and S. Malhotra, Real-Time Human Skin Color Detection Algorithm Using Skin Color Map, 2015.Search in Google Scholar, [46] 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. COVID-19 Coronavirus is a global pandemic that forced a huge percentage of employees to work and contact remotely. The hand tracking is performed by the particle filter that combines hand motion and CNN pre-trained hand . Sign languages (also known as signed languages) are languages that use the visual-manual modality to convey meaning, instead of spoken words. ROI [42]: It is focused on detecting [43] hand gestures and extracting the most interesting points. 10.14569/IJACSA.2011.021108.Search in Google Scholar, [27] O. Koller, J. Forster, and H. Ney, Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers, Computer Vis. M. P. Paulraj, S. Yaacob, Z. Azalan, M. Shuhanaz, and R. Palaniappan, A Phoneme-based Sign Language Recognition System Using Skin Color Segmentation, 2010, pp. 165174.Search in Google Scholar, [61] N. Tubaiz, T. Shanableh, and K. Assaleh, Glove-based continuous Arabic sign language recognition in user-dependent mode, IEEE Trans. The dataset includes digits [010] and 23 alphabets and about 67 most common words. Yuxiao, L. Zhao, X. Peng, J. Yuan, and D. Metaxas, Construct Dynamic Graphs for Hand Gesture Recognition Via Spatial-temporal Attention, UK, 2019, pp. 16.10.1109/IMTC.2007.379311Search in Google Scholar, [56] J. Lim, D. Lee, and B. Kim, Recognizing hand gesture using wrist shapes, 2010 Digest of Technical Papers of the International Conference on Consumer Electronics (ICCE), Las Vegas, 2010, pp. G. Saggio, P. Cavallo, M. Ricci, V. Errico, J. Zea, and M. E. Benalczar, Sign language recognition using wearable electronics: implementing k-Nearest Neighbors with dynamic time warping and convolutional neural network algorithms, Sensors, vol. They may use sign language as their primary way of communication. The priority of the signers selection was based on their skins color difference from background color. 10.1109/etcm.2018.8580268.Search in Google Scholar, [63] L. Chen, J. Fu, Y. Wu, H. Li, and B. 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Castro, CORILSE: a Spanish sign language repository for linguistic analysis, Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), 2016, May, pp. 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. How important are accurate tracking of body parts and its movements? Some of these societies use only one hand for sign languages such as USA, France, and Russia, while others use two-hands like UK, Turkey, and Czech Republic. Ref. 212221. They had collected about 5,829 phrases over 4 phases, with a total number of 9 deployments, each phrase has about 3, 4, or 5 signs taken from a vocabulary token of about 22 signs which is a list of adjectives, objects, prepositions, and subjects. 7, pp. To communicate with deaf people, an interpreter is needed to translate real-world words and sentences. 8, 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. 9, no. Search in Google Scholar, [20] Z. Zafrulla, H. Brashear, P. Yin, P. Presti, T. Starner, and H. Hamilton, American sign language phrase verification in an educational game for deaf children, IEEE, pp. Table 3 discusses some of the KNN algorithms applied on different datasets. Introduction Sign language is a form of communication that utilizes visual-manual methodologies such as expressions, hand gestures, and body movements to interact among the deaf and hard of hearing community, yield opinions, and convey meaningful conversations [ 1 ]. 57, pp. Sign language recognition software must accurately detect these non-manual components. 45, no. Many research problems are suggested in this domain such as Sign Language Recognition (SLR), Sign Language Identification (SLID), Sign Language Synthesis, and Sign Language Translation [10]. The survey also compares some ML techniques with the most used deep learning algorithm (CNN), showing that deep learning results exceed traditional ML. A window of 10 10 around centered pixel of signers face is used to detected skin, but it is not accurate because in most cases it detects nose as it suffers from high illumination conditions [51]. [90] applied CNN algorithm on ISL dataset which consists of distinct 100 images, generating 35,000 images of both colored and grayscale image types. L. K. Tolentino, R. Serfa Juan, A. Thio-ac, M. Pamahoy, J. Forteza, and X. Garcia, Static sign language recognition using deep learning, Int. [48] applied five types of feature extraction including fingertip finder, elongatedness, eccentricity, pixel segmentation, and rotation. CNN algorithms accuracy was 96.2% which is higher than SVM classification algorithm applied by the author to achieve an accuracy of 93.5%. trends, and barrier concerns to 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. It is used to capture many physical data such as hand gestures and postures, body movements, and motion tracker, all these movements are interpreted by software that accompanies the glove. 2, 2021. 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. There is great diversity in sign language execution, based on ethnicity, geographic region, age, gender, education, language prociency, hearing status, etc. Appl. For example, the word drink could be represented similarly in the three languages ASL, ArSL, and SSL [16]. Process, vol. AI Sign : Sign Language on the App Store Search in Google Scholar, [7] J. V. Van Cleve, Gallaudet Encyclopedia of Deaf People and Deafness, Vol 3, New York, New York, McGraw-Hill Company, Inc., 1987, pp. Weather Dataset [40]: A continuous SL composed of three state-of-the-art datasets for the SL recognition purpose: RWTH-PHOENIX-Weather 2012, RWTH-PHOENIX-Weather 2014, and SIGNUM. Classifier model was trained and tested on 8 classes, its accuracy was not high as it ranges between 0.66 and 0.76 for different streams. Your documents are now available to view. 126, pp. Computer Appl., vol. The article addresses the most common datasets used in the literature for the two tasks (static and dynamic datasets that are collected from different corpora) with different contents including numerical, alphabets, words, and sentences from different SLs. 12 (Issue 1), pp. The latter task is targeted to identify the signer language, while the former is aimed to translate the signer conversation into tokens (signs). On the other hand, it has the advantage of integrating new data they gathered from other deployments into its libraries. The following table illustrates this difference. You're going to have to do a lot of data cleaning/filtering before it gets to the HMM, however. Appl., vol. 2, 2005, pp. 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. Skin detection is applied on HSV (HUE, and Saturation Values) images and YCbCr. The upcoming sections are arranged as follows: Datasets of different SLs are described in Section 2. Sign language identification and recognition: A comparative study O.Al-Jarrah and A. Halawani, Recognition of gestures in Arabic sign language using neuro-fuzzy systems, Artif. CNN extracts feature from the frames, using two major approaches for classification as SoftMax layer and the pool layer. 14. p. 3879, 2020. Res. Symp., vol. 126, pp. Ref. Pattern Anal. 19, 2020. [49] applied new technique for feature extraction known as 7Hu moments invariant, which are used as a feature vector of algebraic functions. O. Koller, J. Forster, and H. Ney, Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers, Computer Vis. M. Oliveira, H. Chatbri, Y. Ferstl, M. Farouk, S. Little, N. OConnor, et al., A dataset for Irish sign language recognition, Proceedings of the Irish Machine Vision and Image Processing Conference (IMVIP), vol. R. Rastgoo, K. Kiani, and S. Escalera, Hand sign language recognition using multi-view hand skeleton, Expert. Second, it has no unified grammar (their book contains only a collection of signs without any grammar). Comput., vol. 885889. Deaf. 2, 2011. 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. On the other hand, a word/an expression may have different phonological features in different SLs. The variety of sign language datasets, which includes different gestures, leads to different accuracies as we had discussed based on review of previous literature. Fitri [66] proposed a framework using Simple Multi Attribute Rating Technique (SMART) weighting and KNN classifier. The identification process is considered as a multiclass classification problem. Z. Parcheta and C.-D. Martnez-Hinarejos, Sign language gesture recognition using HMM, in Pattern Recognition and Image Analysis. [44] applied two CNN models on 24 letters of ASL with 10 images per letter, image size is 227 227 which is resized using the Bicubic interpolation method. Extracted features include high performance, flexibility, and stability. 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. 20, pp. Intellig., vol. Mob. 10.1109/ICIEV.2019.8858563.Search in Google Scholar, [86] L. K. S. Tolentino, R. O. Serfa Juan, A. C. Thio-ac, M. A. Signers gestures were captured by digital cameras. Images were scaled to 64 64. Also, recognition time using CNN (0.356s) is lower than SVM (0.647s). 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. 133, pp. Daniel [38] used Raspberry pi with a thermal camera to produce 3,200 images with low resolution of 32 32 pixel. 10.18178/ijmlc.2019.9.6.879.Search in Google Scholar, [89] P. M. Ferreira, J. S. Cardoso, and A. Rebelo, Multimodal Learning for Sign Language Recognition, Pattern Recognition and Image Analysis. Oliveira et al. Z. Onno Crasborn and J. Ros, Corpus-NGT. L. Alexandre, J. Salvador Snchez, J. Rodrigues, (Eds), IbPRIA, vol. Multimedia, pp. S. Ebling, N. C. Camgz, P. B. Braem, K. Tissi, S. Sidler-Miserez, S. Stoll, and M. 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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. JOURNAL OF LA A Comprehensive Review of Sign Language Recognition The most common SLs are American Sign Language (ASL) [3], Spanish Sign Language (SSL) [4], Australian Sign Language (AUSLAN) [5], and Arabic Sign Language (ArSL) [6]. 16. Image Underst., vol. (2022). 110, 2015.Search in Google Scholar, [78] P. P. Roy, P. Kumar, and B. Deaf people have therefore used a kind of auxiliary gestural system for international communication at sporting or cultural events since the early 19th century [18]. S. Wilcox and J. Peyton, American Sign Language as a foreign language, CAL. 526533, 2015.10.1109/THMS.2015.2406692Search in Google Scholar, [62] P. D. Rosero-Montalvo, P. Godoy-Trujillo, E. Flores-Bosmediano, J. Carrascal-Garcia, S. Otero-Potosi, H. 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Traditional and deep learning algorithm results applied on the same dataset. Process, vol. One of them is SLR using signer independent or signer dependent. 13711375, 1998.10.1109/34.735811Search in Google Scholar, [72] C. Zimmermann and T. Brox, Learning to estimate 3D hand pose from single RGB images, 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 10.3390/s20030672.Search in Google Scholar [87] studied the effect of data-augmentation on deep learning algorithms, achieving an accuracy of 97.12% which is higher than that of the model before applying data augmentation by about 4%. Continuous Sign Language Recognition through a Context-Aware - PubMed Process, vol. 9. pp. 2020, pp. L. Chen, J. Fu, Y. Wu, H. Li, and B. Zheng, Hand gesture recognition using compact CNN via surface electromyography signals, Sensors, vol. Sign Language Recognition (shortened generally as SLR) is a computational task that involves recognizing actions from sign languages. This paper presents an isolated sign language recognition system that comprises of two main phases: hand tracking and hand representation. Most widely used electronic devices for hand gesture recognition. For example, the word Stand in American and the word (stand) in Arabic are represented differently in the two SLs. F. Raheem and A. Gebre et al. Applying HMM to recognize and detect each word with 6 models and different Gaussian mixtures. Starting with acquiring an image from the video input stream, then adjusting image size, converting an image from RGB color space to YCbCr space (also denoting that YCbCr space is the most suitable one for skin color detection), and finally identifying color based on different values of threshold [46,47] and marking the detected skin with white color, otherwise with black color. As in spoken language, differ- 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. 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. The Legal Recognition of National Sign Languages - WFD 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. 14021407.Search in Google Scholar, [5] T. Johnston and A. Schembri, Australian Sign Language (Auslan): An Introduction to Sign Language Linguistics, Cambridge, UK, Cambridge University Press, 2007. AI Sign is able to recognise more than 100+ actions (Sign language in word). 20, pp. O. M. Sincan and H. Y. Keles, AUTSL: A large-scale multi-modal Turkish sign language dataset and baseline methods, IEEE Access, vol. 9, no. J. Adv. Appl., vol. Another disadvantage and disability are wearing gloves because users must interact with systems using gloves. Also introduced a new public large-scale dataset for Greek sign language RGB + D, providing two CTC variations that were mostly used in other application fields EnCTC and StimCTC. Yuxiao, L. Zhao, X. Peng, J. Yuan, and D. Metaxas, Construct Dynamic Graphs for Hand Gesture Recognition Via Spatial-temporal Attention, UK, 2019, pp. Univ. Vision-based approach: The great development in computer techniques and ML algorithms motivate many researchers to depend on vision-based methodology. 526533, 2015. Image enhancement such as image filtering, data augmentation, and some other algorithms could be used to detect edges. 7. pp. 212-21. Sci. 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Understanding SL by vocal people not only paves the way to contribute deaf and dumb in the. 1044510453, 2021. 20, no. The legal recognition of signed languages differs widely. [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. 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). The need for an organized and unified SL was first discussed in World Sign Congress in 1951. 10. 821827, 2019.10.18178/ijmlc.2019.9.6.879Search in Google Scholar, [87] K. Wangchuk, P. Riyamongkol, and R. Waranusast, Real-time Bhutanese sign language digits recognition system using convolutional neural network, ICT Exp., vol.