Computer vision based Bengali sign words recognition using contour analysis

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

doi:10.1109/iccitechn.2015.7488092

Cited by 21 publications

(7 citation statements)

References 7 publications

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“…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

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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.

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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

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“…The available BdSL datasets are not sufficient enough [11,12,17,18,19] to develop a fully functioned real-time BdSL detection system. Half of these datasets [11,12,17,20] are also not available for further research.…”

Section: Related Workmentioning

confidence: 99%

“…In Table 1, we compare the BdSL36 with other existing datasets related to the task of BdSL letter recognition. Most of the available BdSL [12,18,19,20,28,29,3] Fig. 3: The top left image is a raw image captured by one of the volunteers.…”

Section: Comparison With Other Datasetsmentioning

confidence: 99%

“…In this section, we first discuss some of the previous works and datasets in automating BdSL letters recognition, followed by reviewing some works on data augmentation. Rahaman et al [12] use 1800 contour templates for 18 Bangladeshi signs separately. The authors introduce a computer vision-based system that applies contour analysis and Haar-like feature-based cascaded classifier and achieve 90.11% recognition accuracy.…”

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%

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BdSL36: A Dataset for Bangladeshi Sign Letters Recognition

Hoque¹,

Jubair²,

Akash³

et al. 2021

Computer Vision – ACCV 2020 Workshops

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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.

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Developing an Optimization Model to Determine Fleet Size of Automated Guided Vehicle

Erdogan,

Tasan

2023

Communications in Computer and Information Science

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Computer vision based Bengali sign words recognition using contour analysis

Cited by 21 publications

References 7 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

BdSL36: A Dataset for Bangladeshi Sign Letters Recognition

Developing an Optimization Model to Determine Fleet Size of Automated Guided Vehicle

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