Benchmarking deep neural network approaches for Indian Sign Language recognition

Sharma, Ashish; Sharma, Nikita; Saxena, Yatharth; Singh, Anuraj; Sadhya, Debanjan

doi:10.1007/s00521-020-05448-8

Cited by 43 publications

(7 citation statements)

References 30 publications

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“…Gesture-based communication is the correspondence interaction for the meeting impeded in the public arena. Different researchers have dealt with gesture-based communications from their individual nations, including American, Chinese, Finnish, British, Italian, Ukrainian, and Arabic, to make a superior world for the hearing impaired [1]. G. Anantha et al [3] they propose the recognition of Indian sign language gestures with the help of convolutional neural networks (CNN).…”

Section: Literature Reviewmentioning

confidence: 99%

“…A Gloss Enhancement Module (GFE) will be acquainted with the proposed network to empower better succession arrangement learning. Ashish et al [1] systematically looked at between three promising deep learning-based methodologies: the pre-prepared VGG16 model, the normal language-based output network, and the hierarchical network for recognizing ISL signals. The hierarchical network outperforms the other two models with a precision of 98.52% for one-handed and 97% for two-handed gestures.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Consequently, there is a correspondence hole that is difficult to shut in standard society. The Indian Deaf Association appraises that around 1,000,000 populaces have some sort of utilitarian hearing misfortune [1].…”

Section: Introductionmentioning

confidence: 99%

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Real-time recognition framework for Indian Sign Language using fine-tuned convolutional neural networks

Soni¹,

Vijay²,

Khandelwal³

et al. 2023

SCRS Proceedings of International Conference of Undergraduate Students

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Sign language is a very crucial aspect in the lives of those who cannot speak or listen to those around them. People with these disabilities have difficulty communicating with the outside world and feel left behind. Much research is ongoing to create a better way of communicating for these people. This work establishes interaction between hearing or speech impaired with the world by recognizing the 33-hand pose and gestures of Indian Sign Language (ISL). This framework can recognize alphabets and numbers in real-time and also generate gestures in real-time for the given alphabets and numbers. The fine-tuned Convolutional Neural Network (CNN) model is explored for the recognition of alphabets and numbers in real-time. A GUI is developed for an easy-to-use interface and immediate visual feedback. Data acquisition software is also developed to create a database. A database of 74,200 images of 33 static signs is captured and used in this work. The results are evaluated on different CNN architectures and learning rates. Accuracy, precision, recall, and F-score are used as performance metrics. The proposed work accomplished the most noteworthy training precision of 99.97% and a validation accuracy of 99.59%.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Real-time recognition framework for Indian Sign Language using fine-tuned convolutional neural networks

Soni¹,

Vijay²,

Khandelwal³

et al. 2023

SCRS Proceedings of International Conference of Undergraduate Students

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“…They collected the dataset for ISL for English language alphabets with a simple and complex background also used this dataset for training and testing deep learning-based CNN and attained good recognition accuracy. Sharma et al 22 carried out a comparative study of various CNN architectures for ISLR tasks under a constrained environment. In this study, the authors trained and tested three models on a selfcreated dataset of 26 English alphabets, pretrained VGG16 with fine-tuning, a hierarchical network model, and a natural language-based output network.…”

Section: Related Workmentioning

confidence: 99%

Optimization of convolutional neural network hyperparameters using improved competitive gray wolf optimizer for recognition of static signs of Indian Sign Language

2023

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In the last decade, the success achieved by convolutional neural networks (CNN) has changed the trend of research in the computer vision field from manual features to learned features for various applications. However, designing the architecture of CNN and selecting optimum values of its hyperparameters poses a critical challenge because of their massive and complex search space. Automatically picking up the optimal values of hyperparameters may facilitate the designers of CNN-based deep learning models. Swarm intelligence (SI)-based improved competitive gray wolf optimizer (ICGWO) in continuous search space is proposed for optimizing hyperparameters of custom-designed CNN architecture. To evaluate the performance of the ICGWO, we employ 16 benchmark functions. We also assess the effectiveness of the ICGWO by evaluating it with six state-of-art meta-heuristic algorithms. The experiment results show that the ICGWO can obtain the best accuracy on eleven benchmark functions and second best on two benchmark functions. The optimized CNN recognizes static signs of English alphabets in Indian Sign Language (ISL). We also evaluated the optimized CNN on the publicly available ISL dataset for the English alphabet, where it attains 99.93% validation accuracy. In order to evaluate the efficacy of the proposed variant of CGWO, we optimized the hyperparameters of CNN using the existing SI-based search optimization algorithms namely particle swarm optimization, whale optimization algorithm (WOA), differential evolution, and gray wolf optimizer as well.

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“…In [13], a comparative analysis of different gesture recognition methods including convolutional neural network (CNN) and machine learning (ML) procedures has been deliberated and verified for realistic performance. A hierarchical neural network, pre-trained VGG16 with fine-tuning, and VGG16 with transfer learning were analyzed according to a trained parameter count.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Sign Language Recognition for Hearing and Speaking Impaired People

Alnfiai¹

2023

Intelligent Automation &Amp; Soft Computing

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Sign language is mainly utilized in communication with people who have hearing disabilities. Sign language is used to communicate with people having developmental impairments who have some or no interaction skills. The interaction via Sign language becomes a fruitful means of communication for hearing and speech impaired persons. A Hand gesture recognition system finds helpful for deaf and dumb people by making use of human computer interface (HCI) and convolutional neural networks (CNN) for identifying the static indications of Indian Sign Language (ISL). This study introduces a shark smell optimization with deep learning based automated sign language recognition (SSODL-ASLR) model for hearing and speaking impaired people. The presented SSODL-ASLR technique majorly concentrates on the recognition and classification of sign language provided by deaf and dumb people. The presented SSODL-ASLR model encompasses a two stage process namely sign language detection and sign language classification. In the first stage, the Mask Region based Convolution Neural Network (Mask RCNN) model is exploited for sign language recognition. Secondly, SSO algorithm with soft margin support vector machine (SM-SVM) model can be utilized for sign language classification. To assure the enhanced classification performance of the SSODL-ASLR model, a brief set of simulations was carried out. The extensive results portrayed the supremacy of the SSODL-ASLR model over other techniques.

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Benchmarking deep neural network approaches for Indian Sign Language recognition

Cited by 43 publications

References 30 publications

Real-time recognition framework for Indian Sign Language using fine-tuned convolutional neural networks

Real-time recognition framework for Indian Sign Language using fine-tuned convolutional neural networks

Optimization of convolutional neural network hyperparameters using improved competitive gray wolf optimizer for recognition of static signs of Indian Sign Language

Deep Learning-Based Sign Language Recognition for Hearing and Speaking Impaired People

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