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Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language

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Abstract

Because of using traditional hand-sign segmentation and classification algorithm, many diversities of Bangla language including joint-letters, dependent vowels etc. and representing 51 Bangla written characters by using only 36 hand-signs, continuous hand-sign-spelled Bangla sign language (BdSL) recognition is challenging. This paper presents a Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language which consists of two phases. First phase is designed for hand-sign classification and the second phase is designed for Bangla language modeling algorithm (BLMA) for automatic recognition of hand-sign-spelled Bangla sign language. In first phase, we have proposed two step classifiers for hand-sign classification using normalized outer boundary vector (NOBV) and window-grid vector (WGV) by calculating maximum inter correlation coefficient (ICC) between test feature vector and pre-trained feature vectors. At first, the system classifies hand-signs using NOBV. If classification score does not satisfy specific threshold then another classifier based on WGV is used. The system is trained using 5,200 images and tested using another (5, 200 × 6) images of 52 hand-signs from 10 signers in 6 different challenging environments achieving mean accuracy of 95.83% for classification with the computational cost of 39.972 milliseconds per frame. In the Second Phase, we have proposed Bangla language modeling algorithm (BLMA) which discovers all “hidden characters” based on “recognized characters” from 52 hand-signs of BdSL to make any Bangla words, composite numerals and sentences in BdSL with no training, only based on the result of first phase. To the best of our knowledge, the proposed system is the first system in BdSL designed on automatic recognition of hand-sign-spelled BdSL for large lexicon. The system is tested for BLMA using hand-sign-spelled 500 words, 100 composite numerals and 80 sentences in BdSL achieving mean accuracy of 93.50%, 95.50% and 90.50% respectively.

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Acknowledgements

This research was partially supported and funded by the Information and Communication Technology (ICT) Division, Ministry of Posts, Telecommunications and IT, Government of the People’s Republic of Bangladesh.

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Correspondence to Muhammad Aminur Rahaman.

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Muhammad Aminur Rahaman received his BSc and MSc degree in Computer Science & Engineering from the Department of Computer Science & Engineering, Islamic University, Bangladesh in 2003 and 2004, respectively. He is a candidate for PhD degree under supervision of Prof. Dr. Md. Hasanuzzaman and Prof. Dr. Md. Haider Ali in the Department of Computer Science & Engineering, University of Dhaka, Bangladesh. He is a founder Director of Worldgaon (Pvt.) Limited which is a one of the famous software development company of Bangladesh. His current research interests including computer vision, sign language recognition and human-computer interaction. He is a member of IEEE.

Mahmood Jasim received his BSc and MSc degree in Computer Science & Engineering from the Department of Computer Science & Engineering, University of Dhaka, Bangladesh in 2011 and 2013, respectively. He joined the Department of Computer science and engineering as a lecturer in 2014. Currently he is pursuing his PhD degree in the University of Massachusetts Amherst. His current research interests include human-computer interaction, image processing, computer vision and artificial intelligence.

Md. Haider Ali received PhD degree from the Department of Electronics & Information Engineering, Toyohashi University of Technology, Japan in 2001. Prof. Ali has completed his Bachelor and Master degree from the Department of Applied Physics & Electronics (presently EEE) University of Dhaka 1984 and 1985 respectively. He is a professor of the Department of Computer Science & Engineering, University of Dhaka since June 2007. His current research interests include human face recognition and expression detection, post surgical expression simulation, soft-tissue deformation modeling, polygonal mesh simplification, narrow band video transmission/video conferencing, etc.

Md. Hasanuzzaman received PhD degree from the Department of Informatics, National Institute of Informatics (NII), The Graduate University for Advanced Studies, Japan. He graduated (with Honors) in 1993 from the Department of Applied Physics & Electronics, University of Dhaka, Bangladesh. He completed Masters of Science (MSc) in Computer Science in 1994 from the University of Dhaka. He joined as a Lecturer in the Department of Computer Science & Engineering, University of Dhaka, Bangladesh in 2000. Since March 2013, he has been serving as a professor in the Department of Computer Science & Engineering, University of Dhaka, Bangladesh. His current research interests include human-computer interaction, image processing, computer vision and artificial intelligence.

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Rahaman, M.A., Jasim, M., Ali, M.H. et al. Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language. Front. Comput. Sci. 14, 143302 (2020). https://doi.org/10.1007/s11704-018-7253-3

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