Towards Inclusive Communication: Reviewing CNN Models for Hindi Varnamala Sign Language Recognition
DOI:
https://doi.org/10.37628/ijaic.v9i2.1951Abstract
Dealing with an individual who has hearing impairments is never easy. This research represents an effort (extension) to investigate the challenges associated with character categorization in Indian Sign Language (ISL).
It is necessary for two people to know and comprehend the same language to create communication. However, sign language is necessary for communication for those who have hearing impairments. The gestures used by the deaf and dumb people are easy for normal people to understand and interpret.
This presentation presents work that attempts to lessen these challenges. Both sides should communicate. In this research, we present a recognition system for Sign Language using Hindi Varnmala. While a great deal of research has been published on American Sign Language, relatively little of it has been
done for those who only use their mother tongue to communicate. In this research, a real-time software system for recognising hand movements in Indian Sign Language (ISL) is introduced. The user must be able to record gestures using a web camera so that the system can determine whether they are correct. An open CV will be used to record a live broadcast. The captured image is processed through several stages, including background subtraction, grey scaling, and other computer vision operations.
References
G. Pala, J. B. Jethwani, S. S. Kumbhar and S. D. Patil, “Machine Learning-based Hand Sign Recognition,” 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore, India, 2021, pp. 356-363, doi: 10.1109/ICAIS50930.2021.9396030.
Lionel Pigou, Sander Dieleman, Pieter- Jan Kindermans and Benjamin Schrauwen, “Sign Language Recognition Using Convolutional Neural Networks,” 2015 European Conference on Computer Vision.
N. K. Bhagat, Y. Vishnusai and G. N. Rathna, “Indian Sign Language Gesture Recognition using Image Processing and Deep Learning,” 2019 Digital Image Computing: Techniques and Applications (DICTA), Perth, WA, Australia, 2019, pp. 1-8, doi: 10.1109/DICTA47822.2019.8945850.
Machine Learning techniques for Indian Sign Language Recognition by Kusumika Krori Dutta. https://doi.org/10.1109/CTCEEC.2017.845
Indian Sign Language Recognition using Convolutional Neural Network.
https://doi.org/10.1051/itmconf/20214003004
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. 4896-4899, doi:
1109/BigData.2018.8622141.
N. Mukai, N. Harada and Y. Chang, “Japanese Fingerspelling Recognition Based on Classification Tree and Machine Learning,” 2017 Nicograph International (NicoInt), Kyoto, Japan, 2017, pp. 19-24,
doi: 10.1109/NICOInt.2017.9.
T. Liu, W. Zhou and H. Li, “Sign language recognition with long short-term memory,” 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 2016, pp. 2871-2875, doi: 10.1109/ICIP.2016.7532884.
J. Zhang, W. Zhou, C. Xie, J. Pu and H. Li, “Chinese sign language recognition with adaptive HMM,” 2016 IEEE International Conference on Multimedia and Expo (ICME), Seattle, WA, USA, 2016, pp. 1-6, doi:
1109/ICME.2016.7552950.
Seema Sabharwal, Priti Singla. Alphabet-Level Indian Sign Language
Translation to Text Using Hybrid-AO Thresholding with CNN. Intelligent
Automation & Soft Computing 2023, 37(3), 2567-2582.
