Abstract
The development of Human-Computer Interaction (HCI) will not only empower the disabled with access to technology but also help to build tools for assisting the disable and their caregivers. Some disabled people are deaf, they use signs to communicate. Signs are emblematic forms which convey a specific meaning. These emblems construct the sign language, which is a non-verbal language used by the deaf community. This research attempts to build a Bangla Sign language recognition system that can recognize signs of both hands. Hands vary in shapes and sizes, as well as signs vary in orientations and motions. Accurate feature extraction is necessary for such systems. For such purposes, deep learning approaches can prove to be effective for the classification and feature engineering of images. In the beginning, an integrated CNN-LSTM model is proposed for building a Sign language Recognition System, a Bangla Sign Language (BSL) dataset consisting of Bangla lexical signs is considered. This dataset consists of 13,400 images comprising thirty-six classes of Bangla lexical signs. The model is trained using a CNN-LSTM model. This model produces a training accuracy of 90% and a testing accuracy of 88.5%. The proposed model is compared to CNN and other CNN state of the art models, namely VGG16, VGG9 and MobileNet. The CNN model and other CNN state undergo the problem of overfitting as their training accuracy is greater than the testing accuracy.
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References
Abedin, M.Z., Nath, A.C., Dhar, P., Deb, K., Hossain, M.S.: License plate recognition system based on contour properties and deep learning model. In: 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp. 590–593 (2017)
Ahmed, T.U., Hossain, S., Hossain, M.S., ul Islam, R., Andersson, K.: Facial expression recognition using convolutional neural network with data augmentation. In: 2019 Joint 8th International Conference on Informatics, Electronics Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision Pattern Recognition (icIVPR), pp. 336–341 (2019)
Al Banna, M.H., Ali Haider, M., Al Nahian, M.J., Islam, M.M., Taher, K.A., Kaiser, M.S.: Camera model identification using deep CNN and transfer learning approach. In: 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), pp. 626–630 (2019)
Al Mamun, S., Fukuda, H., Lam, A., Kobayashi, Y., Kuno, Y.: Autonomous bus boarding robotic wheelchair using bidirectional sensing systems. In: Advances in Visual Computing, pp. 737–747. LNCS, Springer International Publishing (2018)
Andersson, K., Hossain, M.S.: Smart risk assessment systems using belief-rule-based DSS and WSN technologies. In: 2014 4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace & Electronic Systems (VITAE), pp. 1–5. IEEE (2014)
Bour, A., Castillo-Olea, C., Garcia-Zapirain, B., Zahia, S.: Automatic colon polyp classification using convolutional neural network: a case study at basque country. In: 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 1–5. IEEE (2019)
Bovik, A., Huang, T., Munson, D.: A generalization of median filtering using linear combinations of order statistics. IEEE Trans. Acoust. Speech Signal Process. 31(6), 1342–1350 (1983)
Deafness: https://www.who.int/news-room/facts-in-pictures/detail/deafness
Gupta, D., Hossain, E., Hossain, M.S., Andersson, K., Hossain, S.: A digital personal assistant using bangla voice command recognition and face detection. In: 2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), pp. 116–121 (2019)
Haralick, R.M., Sternberg, S.R., Zhuang, X.: Image analysis using mathematical morphology. IEEE Trans. Pattern Anal. Mach. Intell. 4, 532–550 (1987)
Hossain, M.S., Khalid, M.S., Akter, S., Dey, S.: A belief rule-based expert system to diagnose influenza. In: 2014 9th International Forum on Strategic Technology (IFOST), pp. 113–116. IEEE (2014)
Hossain, M.S., Rahaman, S., Kor, A.L., Andersson, K., Pattinson, C.: A belief rule based expert system for datacenter PUE prediction under uncertainty. IEEE Trans. Sustain. Comput. 2(2), 140–153 (2017)
Hossen, M., Govindaiah, A., Sultana, S., Bhuiyan, A.: Bengali sign language recognition using deep convolutional neural network. In: 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 369–373. IEEE (2018)
Kaiser, M.S., et al.: Advances in crowd analysis for urban applications through urban event detection. IEEE Trans. Intell. Transp. Syst. 19(10), 3092–3112 (2018)
Karim, R., Andersson, K., Hossain, M.S., Uddin, M.J., Meah, M.P.: A belief rule based expert system to assess clinical bronchopneumonia suspicion. In: 2016 Future Technologies Conference (FTC), pp. 655–660. IEEE (2016)
Karmokar, B.C., Alam, K.M.R., Siddiquee, M.K., et al.: Bangladeshi sign language recognition employing neural network ensemble. Int. J. Comput. Appl. 58(16), 43–46 (2012)
Mahmud, M., Kaiser, M.S., Hussain, A.: Deep learning in mining biological data. arXiv preprint arXiv:2003.00108 (2020)
Mahmud, M., et al.: A brain-inspired trust management model to assure security in a cloud based IoT framework for neuroscience applications. Cogn. Comput. 10(5), 864–873 (2018)
Mahmud, M., Kaiser, M.S., Hussain, A., Vassanelli, S.: Applications of deep learning and reinforcement learning to biological data. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2063–2079 (2018)
Mamun, S.A., et al.: Terrain recognition for smart wheelchair. In: Intelligent Computing Methodologies, pp. 461–470. LNCS, Springer International Publishing (2016)
Noor, M.B.T., et al.: Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia. Brain Inf. 7(1), 1–21 (2020)
Rahaman, M.A., Jasim, M., Ali, M.H., Hasanuzzaman, M.: Real-time computer vision-based Bengali sign language recognition. In: 2014 17th International Conference on Computer and Information Technology (ICCIT), pp. 192–197. IEEE (2014)
Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Sultana, Z.: Agony of persons with disability—a comparative study of Bangladesh. J. Polit. Law 3, 212 (2010)
Uddin, M.A., Chowdhury, S.A.: Hand sign language recognition for Bangla alphabet using support vector machine. In: 2016 International Conference on Innovations in Science, Engineering and Technology (ICISET), pp. 1–4. IEEE (2016)
Yasir, R., Khan, R.A.: Two-handed hand gesture recognition for Bangla sign language using LDA and ANN. In: The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014), pp. 1–5. IEEE (2014)
Zisad, S.N., Hossain, M.S., Andersson, K.: Speech emotion recognition in neurological disorders using convolutional neural network. In: International Conference on Brain Informatics, pp. 287–296. Springer (2020)
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Basnin, N., Nahar, L., Hossain, M.S. (2021). An Integrated CNN-LSTM Model for Bangla Lexical Sign Language Recognition. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Advances in Intelligent Systems and Computing, vol 1309. Springer, Singapore. https://doi.org/10.1007/978-981-33-4673-4_57
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