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Relevant bibliographies by topics / Bengali sign language

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Academic literature on the topic 'Bengali sign language'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Bengali sign language"

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Ayshee, Tanzila Ferdous, Sadia Afrin Raka, Quazi Ridwan Hasib, Rashedur M. Rahman, and Md Hossain. "Sign Language Recognition for Bengali Characters." International Journal of Fuzzy System Applications 4, no. 4 (2015): 1–14. http://dx.doi.org/10.4018/ijfsa.2015100101.

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Sign language is the primary means of communication for people having speaking and hearing impairment. This language uses a system of manual, facial, and other body movements as the means of communication, as opposed to acoustically conveyed sound patterns. This paper uses image processing and fuzzy logic to develop an intelligent system to recognize Bengali Sign Language. The proposed system works in two phases. In the first phase, the fuzzification methods are defined. Then in the next phase, the raw images are processed to identify the fuzzy rules. A detailed implementation procedure of the

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Rahman, Tanim, Tanjia Chowdhury, and Jeenat Sultana. "Bengali sign language translator with location tracking system." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1760. http://dx.doi.org/10.11591/ijeecs.v33.i3.pp1760-1767.

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Designing an embedded system to convert sign language to sound forms to communicate with the outside world can be a challenging yet rewarding project, especially for mute people. To convey a speaker's thought through sign language, hand shapes, hand orientation and movement, and facial expressions must be combined concurrently. This research is intended to design a system that translates sign language into sound forms to establish communication with the outside world for people who are deaf, those who can hear but cannot physically speak, or have trouble with spoken languages due to some other

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Rahman, Tanim, Tanjia Chowdhury, and Jeenat Sultana. "Bengali sign language translator with location tracking system." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1760–67. https://doi.org/10.11591/ijeecs.v33.i3.pp1760-1767.

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Designing an embedded system to convert sign language to sound forms to communicate with the outside world can be a challenging yet rewarding project, especially for mute people. To convey a speaker's thought through sign language, hand shapes, hand orientation and movement, and facial expressions must be combined concurrently. This research is intended to design a system that translates sign language into sound forms to establish communication with the outside world for people who are deaf, those who can hear but cannot physically speak, or have trouble with spoken languages due to some other

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Raihan, Md Johir, Mainul Islam Labib, Abdullah Al Jaid Jim, Jun Jiat Tiang, Uzzal Biswas, and Abdullah-Al Nahid. "Bengali-Sign: A Machine Learning-Based Bengali Sign Language Interpretation for Deaf and Non-Verbal People." Sensors 24, no. 16 (2024): 5351. http://dx.doi.org/10.3390/s24165351.

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Sign language is undoubtedly a common way of communication among deaf and non-verbal people. But it is not common among hearing people to use sign language to express feelings or share information in everyday life. Therefore, a significant communication gap exists between deaf and hearing individuals, despite both groups experiencing similar emotions and sentiments. In this paper, we developed a convolutional neural network–squeeze excitation network to predict the sign language signs and developed a smartphone application to provide access to the ML model to use it. The SE block provides atte

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Shamrat, F. M. Javed Mehedi, Sovon Chakraborty, Md. Masum Billah, Moumita Kabir, Nazmus Shakib Shadin, and Silvia Sanjana. "Bangla numerical sign language recognition using convolutional neural networks." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (2021): 405–13. https://doi.org/10.11591/ijeecs.v23.i1.pp405-413.

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The amount of deaf and mute individuals on the earth is rising at an alarming rate. Bangladesh has about 2.6 million people who are unable to interact with the community using language. Hearing-impaired citizens in Bangladesh use Bangladeshi sign language (BSL) as a means of communication. In this article, we propose a new method for Bengali sign language recognition based on deep convolutional neural networks. Our framework employs convolutional neural networks (CNN) to learn from the images in our dataset and interpret hand signs from input images. Checking their collections of ten indicatio

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AbdurRahim, Md, Tanzillah Wahid, and Md Khaled Ben Islam. "Visual Recognition of Bengali Sign Language using Artificial Neural Network." International Journal of Computer Applications 94, no. 17 (2014): 1–5. http://dx.doi.org/10.5120/16448-0572.

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Mehedi Shamrat, F. M. Javed, Sovon Chakraborty, Md Masum Billah, Moumita Kabir, Nazmus Shakib Shadin, and Silvia Sanjana. "Bangla numerical sign language recognition using convolutional neural networks (CNNs)." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (2021): 405. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp405-413.

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<p>The amount of deaf and mute individuals on the earth is rising at an alarmingrate. Bangladesh has about 2.6 million people who are unable to interact with the community using language. Hearing-impaired citizens in Bangladesh use Bangladeshi sign language (BSL) as a means of communication. In this article,we propose a new method for Bengali sign language recognition based on deep convolutional neural networks. Our framework employs convolutional neural networks (CNN) to learn from the images in our dataset and interpret hand signs from input images. Checking their collections of ten in

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Afrin, Sanjida. "Semiotic Interpretation of Bangla Ligatures: An Introduction." Dhaka University Journal of Linguistics 2, no. 3 (2010): 111–24. http://dx.doi.org/10.3329/dujl.v2i3.4147.

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Semiotics is the study of sign processes emphasizing signification and communication, signs and symbols of different social phenomena. In the late 19th and early 20th century the works of Ferdinand de Saussure and Charles Sanders Peirce led to the emergence of semiotics as a separate discipline as well as method for examining phenomena in different fields, including aesthetics, anthropology, communications, psychology, and semantics. Saussure's interpretation of linguistic sign from a semiotic perspective has, better or worse, affected much of subsequent discussions about language. But accordi

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Miah, Abu Saleh Musa, Jungpil Shin, Md Al Mehedi Hasan, and Md Abdur Rahim. "BenSignNet: Bengali Sign Language Alphabet Recognition Using Concatenated Segmentation and Convolutional Neural Network." Applied Sciences 12, no. 8 (2022): 3933. http://dx.doi.org/10.3390/app12083933.

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Sign language recognition is one of the most challenging applications in machine learning and human-computer interaction. Many researchers have developed classification models for different sign languages such as English, Arabic, Japanese, and Bengali; however, no significant research has been done on the general-shape performance for different datasets. Most research work has achieved satisfactory performance with a small dataset. These models may fail to replicate the same performance for evaluating different and larger datasets. In this context, this paper proposes a novel method for recogn

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Rahim, Md Abdur, Jungpil Shin, and Keun Soo Yun. "Soft Voting-based Ensemble Model for Bengali Sign Gesture Recognition." Annals of Emerging Technologies in Computing 6, no. 2 (2022): 41–49. http://dx.doi.org/10.33166/aetic.2022.02.003.

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Human hand gestures are becoming one of the most important, intuitive, and essential means of recognizing sign language. Sign language is used to convey different meanings through visual-manual methods. Hand gestures help the hearing impaired to communicate. Nevertheless, it is very difficult to achieve a high recognition rate of hand gestures due to the environment and physical anatomy of human beings such as light condition, hand size, position, and uncontrolled environment. Moreover, the recognition of appropriate gestures is currently considered a major challenge. In this context, this pap

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Book chapters on the topic "Bengali sign language"

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Ashrafi, Arifa, Victor Sergeevich Mokhnachev, Yuriy Nikolaevich Philippovich, and Tsilenko Lyubov Petrovna. "Development of Text-to-Sign Language Translator for Bengali Sign Language." In Software Engineering Application in Informatics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90318-3_63.

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Islam, Muhammad Nazrul, A. M. Shahed Hasan, Tasmiah Tamzid Anannya, Tani Hossain, Marium Binte Ibrahim Ema, and Shoab Ur Rashid. "An Efficient Tool for Learning Bengali Sign Language for Vocally Impaired People." In Mobile Web and Intelligent Information Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27192-3_4.

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Pirdayanti, Ni Putu Ayu, Ni Ketut Anggriani, Komang Jepri Kusuma Jaya, and I. Wayan Pardi. "Developing Sign Language Digital Dictionary “Kolok-Indonesian-English” Through Smart Kolok Application as a Media for Learning and Preserving a Natural Sign Language in Bengkala Village." In Proceedings of the 2nd International Conference on Languages and Arts across Cultures (ICLAAC 2022). Atlantis Press SARL, 2022. http://dx.doi.org/10.2991/978-2-494069-29-9_3.

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Sarwar, Hasan, Mizanur Rahman, Nasreen Akter, Saima Hossain, Sabrina Ahmed, and Chowdhury Mofizur Rahman. "Selection of an Optimal Set of Features for Bengali Character Recognition." In Technical Challenges and Design Issues in Bangla Language Processing. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3970-6.ch005.

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Feature extraction is an essential step of Optical Character Recognition. Accurate and distinguishable feature plays a significant role to leverage the performance of a classifier. The complexity level of feature identification algorithm differs for alphabet sets of different languages. Apart from generic algorithms to find features of different alphabet sets, these algorithms take care of individual characteristic common for a particular alphabet set. Dominant features of one alphabet set might completely differ from that of another set. Since there always remains the chance that inaccurate f

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Conference papers on the topic "Bengali sign language"

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Baranidharan, B., Aman Jha, Joita Ghosh, Srishti Chatterjee, and Prathamesh Patwardhan. "Bengali Sign Language Detection using Deep Learning Techniques." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10723887.

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Akter, Tahmina, Tanjim Mahmud, Tikle Barua, Sultana Rokeya Naher, Mohammad Shahadat Hossain, and Karl Andersson. "Automated Bengali Sign Language Character Classification with Deep Learning Techniques." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724561.

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Abuessa, Md, Md Ruhul Amin Shazid, Shaneen Ara, et al. "Advancing Communication: Deep Learning Innovations in Bengali Sign Language Recognition." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11022491.

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Tanni, Kaniz Fatema, Sanjida Islam, Zinnia Sultana, Tanjina Alam, and Mithila Mahmood. "DeepBdSL: A Comprehensive Assessment of Deep Learning Architectures for Multiclass Bengali Sign Language Gesture Recognition." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11022054.

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Rahaman, Muhammad Aminur, Mahmood Jasim, Md Haider Ali, and Md Hasanuzzaman. "Real-time computer vision-based Bengali Sign Language recognition." In 2014 17th International Conference on Computer and Information Technology (ICCIT). IEEE, 2014. http://dx.doi.org/10.1109/iccitechn.2014.7073150.

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Hossen, M. A., Arun Govindaiah, Sadia Sultana, and Alauddin Bhuiyan. "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). IEEE, 2018. http://dx.doi.org/10.1109/iciev.2018.8640962.

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Tazalli, Tonjih, Zarin Anan Aunshu, Sumaya Sadbeen Liya, et al. "Computer Vision-Based Bengali Sign Language To Text Generation." In 2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS). IEEE, 2022. http://dx.doi.org/10.1109/ipas55744.2022.10052928.

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Perera, Damitha, Bimsara Kanchana, Rojith Peiris, Kasun Madushan, and Dharshana Kasthurirathna. "Review On Hand Gesture Recognition for Bengali Sign Language." In 2022 2nd International Conference on Advanced Research in Computing (ICARC). IEEE, 2022. http://dx.doi.org/10.1109/icarc54489.2022.9753791.

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Aziz, Kazi Ehsan, Ashzabin Wadud, Sadia Sultana, Md Akter Hussain, and Alauddin Bhuiyan. "Bengali Sign Language Recognition using dynamic skin calibration and geometric hashing." In 2017 6th International Conference on Informatics, Electronics and Vision & 2017 7th International Symposium in Computational Medical and Health Technology (ICIEV-ISCMHT). IEEE, 2017. http://dx.doi.org/10.1109/iciev.2017.8338591.

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Hossein, Md Jahangir, and Md Sabbir Ejaz. "Recognition of Bengali Sign Language using Novel Deep Convolutional Neural Network." In 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI). IEEE, 2020. http://dx.doi.org/10.1109/sti50764.2020.9350418.

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