Sanskrit to universal networking language EnConverter system based on deep learning and context-free grammar

Sitender,; Bawa, Seema

doi:10.1007/s00530-020-00692-3

Cited by 8 publications

(2 citation statements)

References 38 publications

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“…CNN is popular for its performance in image classification and therefore has a vast span of applications in various fields [29] such as emotion detection [30], the medical field, the agriculture field [2], Natural Language Translation [31][32][33] and sign language translation. The CNN model is a feedforward artificial neural network used in image recognition tasks because of its optimal performance.…”

Section: Convolution Neural Networkmentioning

confidence: 99%

Alphabet-Level Indian Sign Language Translation to Text Using Hybrid-AO Thresholding with CNN

Sabharwal,

Singla

2023

Intelligent Automation &Amp; Soft Computing

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Sign language is used as a communication medium in the field of trade, defence, and in deaf-mute communities worldwide. Over the last few decades, research in the domain of translation of sign language has grown and become more challenging. This necessitates the development of a Sign Language Translation System (SLTS) to provide effective communication in different research domains. In this paper, novel Hybrid Adaptive Gaussian Thresholding with Otsu Algorithm (Hybrid-AO) for image segmentation is proposed for the translation of alphabet-level Indian Sign Language (ISLTS) with a 5-layer Convolution Neural Network (CNN). The focus of this paper is to analyze various image segmentation (Canny Edge Detection, Simple Thresholding, and Hybrid-AO), pooling approaches (Max, Average, and Global Average Pooling), and activation functions (ReLU, Leaky ReLU, and ELU). 5-layer CNN with Max pooling, Leaky ReLU activation function, and Hybrid-AO (5MXLR-HAO) have outperformed other frameworks. An open-access dataset of ISL alphabets with approx. 31 K images of 26 classes have been used to train and test the model. The proposed framework has been developed for translating alphabet-level Indian Sign Language into text. The proposed framework attains 98.95% training accuracy, 98.05% validation accuracy, and 0.0721 training loss and 0.1021 validation loss and the performance of the proposed system outperforms other existing systems.

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Section: Convolution Neural Networkmentioning

confidence: 99%

Alphabet-Level Indian Sign Language Translation to Text Using Hybrid-AO Thresholding with CNN

Sabharwal,

Singla

2023

Intelligent Automation &Amp; Soft Computing

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“…CNNs exceptional performance is largely attributed to the brilliant design ideas by researchers and the meticulous hyperparameter value choices. Recommendation systems, Natural language processing, are the domains in which CNN is employed [2,3]. CNN is broadly acknowledged by a multitude of parameters and requires exhaustive tuning of hyperparameters like the layer count, batch size, activation function, optimisers, pooling and epoch size [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Optimised Machine Learning-based Translation of Indian Sign Language to Text

2023

IJIES

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Sign language translation through deep learning is a popular topic among researchers nowadays. It opens the doors of communication for deaf and mute people by translating sign language gestures. The translation of input sign language gestures into text is called the sign language translation system (SLTS). In this paper, optimised machine learning-based SLTS for Indian sign language (ISL) has been proposed to facilitate deaf-mute persons. Further, this paper presents a simulation analysis of the impact of the number of convolution layers, size of stride function, epochs, and activation function on the accuracy of translation of ISL gestures. An optimised ISL translation system (ISLTS) for fingerspelled alphanumeric data of 36 classes using a convolution neural network (CNN) with a novel RADAM_NORM optimiser has been proposed. The proposed system has been implemented using two datasets-the first customised ISL alphanumeric dataset has been taken from Kaggle and the second dataset has been prepared by the author consisting of 36 classes and nearly 50K images. The accuracy of the proposed ISLTS on the first dataset is 99.446 % and on the second dataset is 97.889%.

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Indian Sign Language Digit Translation Using CNN with Swish Activation Function

Seema

Singla

2023

Lecture Notes in Networks and Systems

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Sanskrit to universal networking language EnConverter system based on deep learning and context-free grammar

Cited by 8 publications

References 38 publications

Alphabet-Level Indian Sign Language Translation to Text Using Hybrid-AO Thresholding with CNN

Alphabet-Level Indian Sign Language Translation to Text Using Hybrid-AO Thresholding with CNN

Optimised Machine Learning-based Translation of Indian Sign Language to Text

Indian Sign Language Digit Translation Using CNN with Swish Activation Function

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