Bengali Sign Language Recognition using dynamic skin calibration and geometric hashing

Aziz, Kazi Ehsan; Wadud, Ashzabin; Sultana, Sadia; Hussain, Md. Akter; Bhuiyan, Alauddin

doi:10.1109/iciev.2017.8338591

Cited by 12 publications

(4 citation statements)

References 12 publications

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“…Hossen et al presented a CNN-based model for BSL recognition in Hossen, Govindaiah, Sultana, and Bhuiyan (2018). They worked with a diverse and natural color image dataset (Aziz, Wadud, Sultana, Hussain, & Bhuiyan, 2017) with varied background, color and lighting condition making it usable in real time systems. On a comparatively small sized diverse dataset consisting of 1147 images of 37 different signs, they achieved a validation set accuracy of 84.68%.…”

Section: Bangla Sign Language (Bsl)mentioning

confidence: 99%

A hybrid approach for Bangla sign language recognition using deep transfer learning model with random forest classifier

Das

Imtiaz²,

Neom³

et al. 2023

Expert Systems with Applications

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Section: Bangla Sign Language (Bsl)mentioning

confidence: 99%

A hybrid approach for Bangla sign language recognition using deep transfer learning model with random forest classifier

Das

Imtiaz²,

Neom³

et al. 2023

Expert Systems with Applications

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“…Afjal et al [18] utilized HOG features and KNN classifier in the recognition of 38 one-handed BdSL alphabets and 10 digits. Aziz et al [9] executed BdSL recognition using dynamic skin calibration and geometric hashing for 37 one-handed BdSL recognition. Many researchers applied CNN to recognize BdSL.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Bangla is the fifth largest language by the total number of native speakers, and Bangla Sign Language (BdSL) avails almost 2.6 million people [8]. It is worth noting that there exist two methods through which BdSL can be represented; one includes the use of one hand to demonstrate signs (named as one-handed method) and the other includes two hands to demonstrate signs (named two handed method) [9]. Because of its simplicity, the one-handed method of BdSL representation is more popular and used extensively.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of a Humanoid Robot for Sign Language Interpretation

et al. 2021

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Purpose With an indisputable complexity of communication for hearing and speaking impaired people, most sign language recognition systems utilize virtual reality or onscreen robots. This paper presents the design and development of a special and low-cost humanoid robot that can perform as a sign language interpreter. To the best of our knowledge, this is the first endeavor to fabricate a humanoid robot for Bangla sign language (BdSL) and Medical signs interpretation. Methods Considering the plethora of design criteria and balancing between rigidity and flexibility 3D models of the robotics parts are designed and 3D printed ensuring cost efficiency. With the help of modern fabrication technology, the robot is developed and assembled with proper actuators and circuitry. An image dataset is built comprising 950 images for BdSL recognition and made publicly available. We utilized the Recurrent neural network (RNN) and Convolutional neural network (CNN) for deep learning model establishment and feature extraction from video and image data. Results The developed humanoid robot has 43 Degrees of freedom (DoF) which includes two 15 DoF hands. It can imitate 16 BdSL alphabets in sign language, can capture a video or image input in real-time from the user, and recognize 10 medical signs and 38 alphabets of BdSL. The learning model for video-based medical sign recognition achieved 87.5% test accuracy. Image-based Bangla sign language recognition achieved an overall test accuracy of 98.19% in our dataset and 93.8% in another available dataset. Conclusion Compared to the state-of-the-art robotic systems for sign language interpretation, our approach has achieved higher kinematic characteristics, remarkable results in sign recognition, and impressive competency in sign imitation; all at almost 10 times lower cost than the state-of-the-art systems. The results are evidence that our approach is efficient and suitable in helping hearing and speaking impaired people. Moreover, this work initiates a research scope that can be further extended for creating equal opportunities for the hearing and speaking impaired community.

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“…Hash table was trained with 1147 samples of 37 alphabets. The hand sign is identified by hashing [17]. For working with images of different backgrounds, a model was proposed by Haque et al which identifies two handed signs using PCA algorithm and ANN.…”

Section: Related Workmentioning

confidence: 99%

Automated Bangla sign language translation system for alphabets by means of MobileNet

et al. 2020

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Individuals with hearing and speaking impairment communicate using sign language. The movement of hand, body and expressions of face are the means by which the people, who are unable to hear and speak, can communicate. Bangla sign alphabets are formed with one or two hand movements. There are some features which differentiates the signs. To detect and recognize the signs, analyzing its shape and comparing its features is necessary. This paper aims to propose a model and build a computer systemthat can recognize Bangla Sign Lanugage alphabets and translate them to corresponding Bangla letters by means of deep convolutional neural network (CNN). CNN has been introduced in this model in form of a pre-trained model called "MobileNet" which produced an average accuracy of 95.71% in recognizing 36 Bangla Sign Language alphabets.

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Bengali Sign Language Recognition using dynamic skin calibration and geometric hashing

Cited by 12 publications

References 12 publications

A hybrid approach for Bangla sign language recognition using deep transfer learning model with random forest classifier

A hybrid approach for Bangla sign language recognition using deep transfer learning model with random forest classifier

Design and Development of a Humanoid Robot for Sign Language Interpretation

Automated Bangla sign language translation system for alphabets by means of MobileNet

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