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

Rahaman, Muhammad Aminur; Jasim, Mahmood; Ali, Md. Yousuf; Hasanuzzaman, Md.

doi:10.1007/s11704-018-7253-3

Cited by 29 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Refs. [11][12][13] discussed these two issues and proposed a computer vision-based solution in Bangla Sign Language Recognition. Application of OpenNI framework and Artificial Neural Network on images captured using Kinect for recognition of few Bangla Sign words was proposed in Choudhury et al [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model

Podder

Chowdhury

Tahir

et al. 2022

Sensors

View full text Add to dashboard Cite

A real-time Bangla Sign Language interpreter can enable more than 200 k hearing and speech-impaired people to the mainstream workforce in Bangladesh. Bangla Sign Language (BdSL) recognition and detection is a challenging topic in computer vision and deep learning research because sign language recognition accuracy may vary on the skin tone, hand orientation, and background. This research has used deep machine learning models for accurate and reliable BdSL Alphabets and Numerals using two well-suited and robust datasets. The dataset prepared in this study comprises of the largest image database for BdSL Alphabets and Numerals in order to reduce inter-class similarity while dealing with diverse image data, which comprises various backgrounds and skin tones. The papers compared classification with and without background images to determine the best working model for BdSL Alphabets and Numerals interpretation. The CNN model trained with the images that had a background was found to be more effective than without background. The hand detection portion in the segmentation approach must be more accurate in the hand detection process to boost the overall accuracy in the sign recognition. It was found that ResNet18 performed best with 99.99% accuracy, precision, F1 score, sensitivity, and 100% specificity, which outperforms the works in the literature for BdSL Alphabets and Numerals recognition. This dataset is made publicly available for researchers to support and encourage further research on Bangla Sign Language Interpretation so that the hearing and speech-impaired individuals can benefit from this research.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model

Podder

Chowdhury

Tahir

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Rahaman et al [13] proposed Haar-like feature-based classifier to recognize 36 alphabets of two-handed BdSL recognition. In another work, Rahaman et al [14] proposed a Bangla language modeling algorithm (BLMA) for 36 two-handed BdSL alphabet and 10 BdSL digits recognition. Both Jarman et al [15] and Ahmed and Akhand [16] implemented feed-forward ANN to recognize one-handed BdSL alphabets.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Development of a Humanoid Robot for Sign Language Interpretation

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

“…But these classifiers only work in a controlled lab environment. In [13], a method of recognizing Hand-Sign-Spelled Bangla language is introduced. The system is divided into two phaseshand sign classification and automatic recognition of hand-sign-spelled for BdSL using the Bangla Language Modeling Algorithm (BLMA).…”

Section: Related Workmentioning

confidence: 99%

“…But, to train a deep learning classifier, the role of a large dataset is unavoidable and plays a pivotal role to build a robust classifier. However, deep learning methods, so far, on BdSL letters recognition are restricted to constraint or small scale dataset [11,12,13], which is not useful to build a robust classifier. The collection of sign letter data is challenging due to the lack of open-source information available on the internet, human resources, the deaf community and knowledge.…”

Section: Introductionmentioning

confidence: 99%

BdSL36: A Dataset for Bangladeshi Sign Letters Recognition

Hoque¹,

Jubair²,

Akash³

et al. 2021

Computer Vision – ACCV 2020 Workshops

View full text Add to dashboard Cite

Bangladeshi Sign Language (BdSL) is a commonly used medium of communication for the hearing-impaired people in Bangladesh. A real-time BdSL interpreter with no controlled lab environment has a broad social impact and an interesting avenue of research as well. Also, it is a challenging task due to the variation in different subjects (age, gender, color, etc.), complex features, and similarities of signs and clustered backgrounds. However, the existing dataset for BdSL classification task is mainly built in a lab friendly setup which limits the application of powerful deep learning technology. In this paper, we introduce a dataset named BdSL36 which incorporates background augmentation to make the dataset versatile and contains over four million images belonging to 36 categories. Besides, we annotate about 40, 000 images with bounding boxes to utilize the potentiality of object detection algorithms. Furthermore, several intensive experiments are performed to establish the baseline performance of our BdSL36. Moreover, we employ beta testing of our classifiers at the user level to justify the possibilities of real-world application with this dataset. We believe our BdSL36 will expedite future research on practical sign letter classification. We make the datasets and all the pre-trained models available for further researcher.

show abstract

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

Cited by 29 publications

References 39 publications

Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model

Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model

Design and Development of a Humanoid Robot for Sign Language Interpretation

BdSL36: A Dataset for Bangladeshi Sign Letters Recognition

Contact Info

Product

Resources

About