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Title: :  CNN-BASED SIGN LANGUAGE RECOGNITION SYSTEM USING MACHINE LEARNING
PaperId: :  20587
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 3 2023
DUI:    16.0415/IJARIIE-20587
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Nikita MandageHSBPVTs COE, Kashti
Shreya SasaneHSBPVTs COE, Kashti
Sakshi RansingHSBPVTs COE, Kashti
Poonam PawarHSBPVTs COE, Kashti
Prof. Bhosale S.S.HSBPVTs COE, Kashti

Abstract

Computer Engineering
Convolutional Neural Networks, Sign language recognition, Signer dependent and Signer independent.
Dynamic head and hand movements, as well as their continually shifting shapes, are regarded a difficult topic in computer vision. Hand segmentation, hand form feature illustration, and gesture sequences recognition are the three key issues in developing an effective sign language system that can distinguish dynamic standalone motions. These methods use colour scheme hand different algorithms to divide hands into segments, hand-crafted features for hand form representations, and Hidden Markov Model (HMM) sequence recognition for traditional sign language identification. a Convolutional neural network (CNN) can be used to recognise Indian sign language motions, according to this article (CNN). It's never easy to have a meaningful conversation with someone who has hearing loss. People with speech and hearing disabilities can use sign language to communicate their thoughts and feelings to the world, making it the ultimate remedy. It facilitates and simplifies the process of integrating them with the rest of society. It is not enough, however, that sign language has been invented. As a result, there are a lot of strings connected. For people who have never learned sign language or who are fluent in a different language, the sign movements can be difficult to decipher. Various strategies for automating the identification of sign motions have made it possible to close the long-standing communication gap.

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IJARIIE Nikita Mandage, Shreya Sasane, Sakshi Ransing, Poonam Pawar, and Prof. Bhosale S.S.. "CNN-BASED SIGN LANGUAGE RECOGNITION SYSTEM USING MACHINE LEARNING" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 3 2023 Page 3079-3085
MLA Nikita Mandage, Shreya Sasane, Sakshi Ransing, Poonam Pawar, and Prof. Bhosale S.S.. "CNN-BASED SIGN LANGUAGE RECOGNITION SYSTEM USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 9.3(2023) : 3079-3085.
APA Nikita Mandage, Shreya Sasane, Sakshi Ransing, Poonam Pawar, & Prof. Bhosale S.S.. (2023). CNN-BASED SIGN LANGUAGE RECOGNITION SYSTEM USING MACHINE LEARNING. International Journal Of Advance Research And Innovative Ideas In Education, 9(3), 3079-3085.
Chicago Nikita Mandage, Shreya Sasane, Sakshi Ransing, Poonam Pawar, and Prof. Bhosale S.S.. "CNN-BASED SIGN LANGUAGE RECOGNITION SYSTEM USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 3 (2023) : 3079-3085.
Oxford Nikita Mandage, Shreya Sasane, Sakshi Ransing, Poonam Pawar, and Prof. Bhosale S.S.. 'CNN-BASED SIGN LANGUAGE RECOGNITION SYSTEM USING MACHINE LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 3, 2023, p. 3079-3085. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/CNN_BASED_SIGN_LANGUAGE_RECOGNITION_SYSTEM_USING_MACHINE_LEARNING_ijariie20587.pdf (Accessed : ).
Harvard Nikita Mandage, Shreya Sasane, Sakshi Ransing, Poonam Pawar, and Prof. Bhosale S.S.. (2023) 'CNN-BASED SIGN LANGUAGE RECOGNITION SYSTEM USING MACHINE LEARNING', International Journal Of Advance Research And Innovative Ideas In Education, 9(3), pp. 3079-3085IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/CNN_BASED_SIGN_LANGUAGE_RECOGNITION_SYSTEM_USING_MACHINE_LEARNING_ijariie20587.pdf (Accessed : )
IEEE Nikita Mandage, Shreya Sasane, Sakshi Ransing, Poonam Pawar, and Prof. Bhosale S.S., "CNN-BASED SIGN LANGUAGE RECOGNITION SYSTEM USING MACHINE LEARNING," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 3, pp. 3079-3085, May-Jun 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/CNN_BASED_SIGN_LANGUAGE_RECOGNITION_SYSTEM_USING_MACHINE_LEARNING_ijariie20587.pdf [Accessed : ].
Turabian Nikita Mandage, Shreya Sasane, Sakshi Ransing, Poonam Pawar, and Prof. Bhosale S.S.. "CNN-BASED SIGN LANGUAGE RECOGNITION SYSTEM USING MACHINE LEARNING." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 3 ().
Vancouver Nikita Mandage, Shreya Sasane, Sakshi Ransing, Poonam Pawar, and Prof. Bhosale S.S.. CNN-BASED SIGN LANGUAGE RECOGNITION SYSTEM USING MACHINE LEARNING. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : ]; 9(3) : 3079-3085. Available from: https://ijariie.com/AdminUploadPdf/CNN_BASED_SIGN_LANGUAGE_RECOGNITION_SYSTEM_USING_MACHINE_LEARNING_ijariie20587.pdf
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