Sign language identification using image processing techniques

In everyday life, computers handle a large amount of data from different sources and formats such as sensors, databases, social networks, texts, etc. In addition to this process, people need to use different communication devices that enrich and facilitate human-computer interaction (HCI). As a resu...

Full description

Autores:
amelec, viloria
Sanchez-Alarcon, Evelyn
Pineda, Omar
Tipo de recurso:
http://purl.org/coar/resource_type/c_816b
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7272
Acceso en línea:
https://hdl.handle.net/11323/7272
https://repositorio.cuc.edu.co/
Palabra clave:
Genetic algorithm
Image processing techniques
Sign language identification
Rights
closedAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_fdc10ec28769ed21012a9aba9be3a40a
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7272
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Sign language identification using image processing techniques
title Sign language identification using image processing techniques
spellingShingle Sign language identification using image processing techniques
Genetic algorithm
Image processing techniques
Sign language identification
title_short Sign language identification using image processing techniques
title_full Sign language identification using image processing techniques
title_fullStr Sign language identification using image processing techniques
title_full_unstemmed Sign language identification using image processing techniques
title_sort Sign language identification using image processing techniques
dc.creator.fl_str_mv amelec, viloria
Sanchez-Alarcon, Evelyn
Pineda, Omar
dc.contributor.author.spa.fl_str_mv amelec, viloria
Sanchez-Alarcon, Evelyn
Pineda, Omar
dc.subject.spa.fl_str_mv Genetic algorithm
Image processing techniques
Sign language identification
topic Genetic algorithm
Image processing techniques
Sign language identification
description In everyday life, computers handle a large amount of data from different sources and formats such as sensors, databases, social networks, texts, etc. In addition to this process, people need to use different communication devices that enrich and facilitate human-computer interaction (HCI). As a result, there is a need to develop computational techniques that allow the search for patterns or characteristic data in images, audio waves, or electrical pulses, among others, to carry out tasks that only humans can do better so far. In this way, to improve both the User Experience (UE) and the ease of interaction with computers, various approaches to natural interaction have been proposed, including digital image processing and acquisition from various data sources such as a sensor like Kinect. In this study, the processing of images obtained from a digital camera is approached to characterize them by using basic computer vision techniques. The paper presents the development of a prototype for supporting people who speak sign language to know if the sign they are doing is correct.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-11-11T22:46:34Z
dc.date.available.none.fl_str_mv 2020-11-11T22:46:34Z
dc.date.issued.none.fl_str_mv 2020
dc.date.embargoEnd.none.fl_str_mv 2021-05-07
dc.type.spa.fl_str_mv Pre-Publicación
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_816b
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dc.identifier.issn.spa.fl_str_mv 2194-5357
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7272
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 2194-5357
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/7272
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv Deshpande, A.M., Kalbhor, S.R.: Video-based marathi sign language recognition and text conversion using convolutional neural network. In: Emerging Trends in Electrical, Communications, and Information Technologies, pp. 761–773. Springer, Singapore (2020)
Paudyal, P., Lee, J., Kamzin, A., Soudki, M., Banerjee, A., Gupta, S.K.: Learn2Sign: explainable AI for sign language learning. In: IUI Workshops (2019)
Naren, J., Venkatesan, R., Rajendran, P., Vasudha, G.S.: Indian sign language spelling finger recognition system. In: Smart Systems and IoT: Innovations in Computing, pp. 845–855. Springer, Singapore (2020)
Ibrahim, N.B., Zayed, H.H., Selim, M.M.: Advances, challenges and opportunities in continuous sign language recognition. J. Eng. Appl. Sci. 15(5), 1205–1227 (2020)
Oz, C., Leu, M.C.: American Sign Language word recognition with a sensory glove using artificial neural networks. Eng. Appl. Artif. Intell. 24(7), 1204–1213 (2011)
Ahuja, R., Jain, D., Sachdeva, D., Garg, A., Rajput, C.: Convolutional neural network based american sign language static hand gesture recognition. Int. J. Ambient Comput. Intell. (IJACI) 10(3), 60–73 (2019)
Sombandith, V., Walairacht, A., Walairacht, S.: Hand gesture recognition for Lao alphabet sign language using HOG and correlation. In: 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 649–651. IEEE (2017)
Pansare, J.R., Ingle, M.: Vision-based approach for American sign language recognition using edge orientation histogram. In: 2016 International Conference on Image, Vision and Computing (ICIVC), pp. 86–90. IEEE (2016)
Varela, N., Silva, J., Gonzalez, F.M., Palencia, P., Palma, H.H., Pineda, O.B.: Method for the recovery of images in databases of rice grains from visual content. Procedia Comput. Sci. 170, 983–988 (2020)
Zafrulla, Z., Brashear, H., Starner, T., Hamilton, H., Presti, P.: American sign language recognition with the kinect. In: Proceedings of the 13th International Conference on Multimodal Interfaces, pp. 279–286 (2011)
Cheok, M.J., Omar, Z., Jaward, M.H.: A review of hand gesture and sign language recognition techniques. Int. J. Mach. Learn. Cybern. 10(1), 131–153 (2019)
Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)
Bhavsar, H., Trivedi, D.: Indian sign language alphabets recognition from static images using correlation-coefficient algorithm with neuro-fuzzy approach, 18 May 2019
Starner, T., Weaver, J., Pentland, A.: Real-time american sign language recognition using desk and wearable computer-based video. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1371–1375 (1998)
Hassan, S.T., Abolarinwa, J.A., Alenoghena, C.O., Bala, S.A., David, M., Enenche, P.: Intelligent sign language recognition using image processing techniques: a case of Hausa Sign language. ATBU J. Sci. Technol. Educ. 6(2), 127–134 (2018)
Dreuw, P., Stein, D., Deselaers, T., Rybach, D., Zahedi, M., Bungeroth, J., Ney, H.: Spoken language processing techniques for sign language recognition and translation. Technol. Disabil. 20(2), 121–133 (2008)
Rajam, P.S., Balakrishnan, G.: Real time Indian sign language recognition system to aid deaf-dumb people. In: 2011 IEEE 13th International Conference on Communication Technology, pp. 737–742. IEEE (2011)
Jiang, S., Gao, Q., Liu, H., Shull, P.B.: A novel, co-located EMG-FMG-sensing wearable armband for hand gesture recognition. Sens. Actuators A: Phys. 301, 111738 (2020)
Shah, T.J., Banday, M.T.: Empirical performance analysis of wavelet transform coding-based image compression techniques. In: Examining Fractal Image Processing and Analysis, pp. 57–99. IGI Global (2020)
Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-Means for innovation databases in SMEs. ANT/EDI40, pp. 1201–1206 (2019)
Munib, Q., Habeeb, M., Takruri, B., Al-Malik, H.A.: American sign language (ASL) recognition based on Hough transform and neural networks. Expert Syst. Appl. 32(1), 24–37 (2007)
Jin, C.M., Omar, Z., Jaward, M.H.: A mobile application of American sign language translation via image processing algorithms. In: 2016 IEEE Region 10 Symposium (TENSYMP), pp. 104–109. IEEE (2016)
Jo, J., Jadidi, Z.: A high precision crack classification system using multi-layered image processing and deep belief learning. Struct. Infrastructure Eng. 16(2), 297–305 (2020)
Li, K.F., Lothrop, K., Gill, E., Lau, S.: A web-based sign language translator using 3d video processing. In: 2011 14th International Conference on Network-Based Information Systems, pp. 356–361. IEEE (2011)
Nikam, A.S., Ambekar, A.G.: Sign language recognition using image based hand gesture recognition techniques. In: 2016 Online International Conference on Green Engineering and Technologies (IC-GET), pp. 1–5. IEEE (2016)
Al-Jarrah, O., Halawani, A.: Recognition of gestures in Arabic sign language using neuro-fuzzy systems. Artif. Intell. 133(1–2), 117–138 (2001)
Kartheek, M.N., Prasad, M.V., Bhukya, R.: Local optimal oriented pattern for person independent facial expression recognition. In: Twelfth International Conference on Machine Vision (ICMV 2019), vol. 11433, p. 114330R. International Society for Optics and Photonics (2020)
Gupta, R., Rana, S., Gupta, S., Pandey, K., Dabas, C.: Comparative analysis of various classifiers for gesture recognition. In: Intelligent Computing Techniques for Smart Energy Systems, pp. 85–94. Springer, Singapore (2020)
Sokhib, T., Whangbo, T.K.: A combined method of skin-and depth-based hand gesture recognition. Int. Arab J. Inf. Technol. 17(1), 137–145 (2020)
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spelling amelec, viloria2f22a05451ff1bbfc2d4dd00035c952fSanchez-Alarcon, Evelyn7fca0175564ff52b3fd26bf81a97987d300Pineda, Omaraf4b322b3d3157067b1e466da357fb982020-11-11T22:46:34Z2020-11-11T22:46:34Z20202021-05-072194-5357https://hdl.handle.net/11323/7272Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In everyday life, computers handle a large amount of data from different sources and formats such as sensors, databases, social networks, texts, etc. In addition to this process, people need to use different communication devices that enrich and facilitate human-computer interaction (HCI). As a result, there is a need to develop computational techniques that allow the search for patterns or characteristic data in images, audio waves, or electrical pulses, among others, to carry out tasks that only humans can do better so far. In this way, to improve both the User Experience (UE) and the ease of interaction with computers, various approaches to natural interaction have been proposed, including digital image processing and acquisition from various data sources such as a sensor like Kinect. In this study, the processing of images obtained from a digital camera is approached to characterize them by using basic computer vision techniques. The paper presents the development of a prototype for supporting people who speak sign language to know if the sign they are doing is correct.application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbAdvances in Intelligent Systems and Computinghttps://www.scopus.com/record/display.uri?eid=2-s2.0-85089223379&doi=10.1007%2f978-3-030-51859-2_8&origin=inward&txGid=ec229328b397a5093dc54502daab03ecGenetic algorithmImage processing techniquesSign language identificationSign language identification using image processing techniquesPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionDeshpande, A.M., Kalbhor, S.R.: Video-based marathi sign language recognition and text conversion using convolutional neural network. In: Emerging Trends in Electrical, Communications, and Information Technologies, pp. 761–773. Springer, Singapore (2020)Paudyal, P., Lee, J., Kamzin, A., Soudki, M., Banerjee, A., Gupta, S.K.: Learn2Sign: explainable AI for sign language learning. In: IUI Workshops (2019)Naren, J., Venkatesan, R., Rajendran, P., Vasudha, G.S.: Indian sign language spelling finger recognition system. In: Smart Systems and IoT: Innovations in Computing, pp. 845–855. Springer, Singapore (2020)Ibrahim, N.B., Zayed, H.H., Selim, M.M.: Advances, challenges and opportunities in continuous sign language recognition. J. Eng. Appl. Sci. 15(5), 1205–1227 (2020)Oz, C., Leu, M.C.: American Sign Language word recognition with a sensory glove using artificial neural networks. Eng. Appl. Artif. Intell. 24(7), 1204–1213 (2011)Ahuja, R., Jain, D., Sachdeva, D., Garg, A., Rajput, C.: Convolutional neural network based american sign language static hand gesture recognition. Int. J. Ambient Comput. Intell. (IJACI) 10(3), 60–73 (2019)Sombandith, V., Walairacht, A., Walairacht, S.: Hand gesture recognition for Lao alphabet sign language using HOG and correlation. In: 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 649–651. IEEE (2017)Pansare, J.R., Ingle, M.: Vision-based approach for American sign language recognition using edge orientation histogram. In: 2016 International Conference on Image, Vision and Computing (ICIVC), pp. 86–90. IEEE (2016)Varela, N., Silva, J., Gonzalez, F.M., Palencia, P., Palma, H.H., Pineda, O.B.: Method for the recovery of images in databases of rice grains from visual content. Procedia Comput. Sci. 170, 983–988 (2020)Zafrulla, Z., Brashear, H., Starner, T., Hamilton, H., Presti, P.: American sign language recognition with the kinect. In: Proceedings of the 13th International Conference on Multimodal Interfaces, pp. 279–286 (2011)Cheok, M.J., Omar, Z., Jaward, M.H.: A review of hand gesture and sign language recognition techniques. Int. J. Mach. Learn. Cybern. 10(1), 131–153 (2019)Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)Bhavsar, H., Trivedi, D.: Indian sign language alphabets recognition from static images using correlation-coefficient algorithm with neuro-fuzzy approach, 18 May 2019Starner, T., Weaver, J., Pentland, A.: Real-time american sign language recognition using desk and wearable computer-based video. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1371–1375 (1998)Hassan, S.T., Abolarinwa, J.A., Alenoghena, C.O., Bala, S.A., David, M., Enenche, P.: Intelligent sign language recognition using image processing techniques: a case of Hausa Sign language. ATBU J. Sci. Technol. Educ. 6(2), 127–134 (2018)Dreuw, P., Stein, D., Deselaers, T., Rybach, D., Zahedi, M., Bungeroth, J., Ney, H.: Spoken language processing techniques for sign language recognition and translation. Technol. Disabil. 20(2), 121–133 (2008)Rajam, P.S., Balakrishnan, G.: Real time Indian sign language recognition system to aid deaf-dumb people. In: 2011 IEEE 13th International Conference on Communication Technology, pp. 737–742. IEEE (2011)Jiang, S., Gao, Q., Liu, H., Shull, P.B.: A novel, co-located EMG-FMG-sensing wearable armband for hand gesture recognition. Sens. Actuators A: Phys. 301, 111738 (2020)Shah, T.J., Banday, M.T.: Empirical performance analysis of wavelet transform coding-based image compression techniques. In: Examining Fractal Image Processing and Analysis, pp. 57–99. IGI Global (2020)Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-Means for innovation databases in SMEs. ANT/EDI40, pp. 1201–1206 (2019)Munib, Q., Habeeb, M., Takruri, B., Al-Malik, H.A.: American sign language (ASL) recognition based on Hough transform and neural networks. Expert Syst. Appl. 32(1), 24–37 (2007)Jin, C.M., Omar, Z., Jaward, M.H.: A mobile application of American sign language translation via image processing algorithms. In: 2016 IEEE Region 10 Symposium (TENSYMP), pp. 104–109. IEEE (2016)Jo, J., Jadidi, Z.: A high precision crack classification system using multi-layered image processing and deep belief learning. Struct. Infrastructure Eng. 16(2), 297–305 (2020)Li, K.F., Lothrop, K., Gill, E., Lau, S.: A web-based sign language translator using 3d video processing. In: 2011 14th International Conference on Network-Based Information Systems, pp. 356–361. IEEE (2011)Nikam, A.S., Ambekar, A.G.: Sign language recognition using image based hand gesture recognition techniques. In: 2016 Online International Conference on Green Engineering and Technologies (IC-GET), pp. 1–5. IEEE (2016)Al-Jarrah, O., Halawani, A.: Recognition of gestures in Arabic sign language using neuro-fuzzy systems. Artif. Intell. 133(1–2), 117–138 (2001)Kartheek, M.N., Prasad, M.V., Bhukya, R.: Local optimal oriented pattern for person independent facial expression recognition. In: Twelfth International Conference on Machine Vision (ICMV 2019), vol. 11433, p. 114330R. International Society for Optics and Photonics (2020)Gupta, R., Rana, S., Gupta, S., Pandey, K., Dabas, C.: Comparative analysis of various classifiers for gesture recognition. In: Intelligent Computing Techniques for Smart Energy Systems, pp. 85–94. Springer, Singapore (2020)Sokhib, T., Whangbo, T.K.: A combined method of skin-and depth-based hand gesture recognition. Int. Arab J. Inf. 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