Sign Language Recognition Using Convolutional Neural Network

Nandhini, A. Sunitha; Roopan, D Shiva; Shiyaam, S; Yogesh, S

doi:10.1088/1742-6596/1916/1/012091

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…In [20], design a recognition system for American sign language using CNN with 125 words sign. In [21], design a recognition system for American sign language using CNN with standard American sign alphabetic letters.…”

Section: Related Workmentioning

confidence: 99%

Automatic Translation From Iraqi Sign Language to Arabic Text or Speech Using CNN

2023

IJCCCE

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Sign language (SL) is Non-verbal communication and a way for the deaf and mute to communicate without words. A deaf and mute person's hands, face, and body shows what they want to say. Since the number of deaf and dumb people is increasing, there must be other ways to learn sign language or communicate with deaf and dumb people. One of these ways is using advanced technology to produce systems that help the deaf/dumb, such as creating recognition and sign language translators. This paper presents an application that works on the computer for machine translation of Iraqi sign language in two directions from sign language to Arabic language (text/speech) and from Arabic language(text) to Iraqi sign language. The proposed system uses a Convolution Neural Network (CNN) to classify sign language based on its features to predicate the sign meaning. The sign language to Arabic language(text/speech) part of the proposed system has an accuracy of 99.3% for letters. Index Terms—Sign Language, Iraqi Sign Language, Deep Learning, CNN, gtts.

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Section: Related Workmentioning

confidence: 99%

Automatic Translation From Iraqi Sign Language to Arabic Text or Speech Using CNN

2023

IJCCCE

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“…The CNN network feature is generated by convoluting the output of the kernel layer with the layer below it, so that the kernel in the first hidden layer executes the convolution on the input image. While early hidden layers typically capture shapes, curves, or edges as a feature, deeper hidden layers generally capture more abstract and intricate information (Nandhini et al, 2021). Traditional approaches to automating image classification involve complex rule-based algorithms or the creation of human features (Chau et al, 2020), which takes time, has limited ability to generalize, and requires subject expertise.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Views on Deep Learning for Medical Image Diagnosis

Nozomi

Aldi

Sentosa

2022

JAETS

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Deep learning models are more often used in the medical field as a result of the rapid development of machine learning, graphics processing technologies, and accessibility of medical imaging data. The convolutional neural network (CNN)-based design, adopted by the medical imaging community to assist doctors in identifying the disease, has exacerbated this situation. This research uses a qualitative methodology. The information used in this study, which explores the ideas of deep learning and convolutional neural networks (CNN), taken from publications or papers on artificial intelligent (AI) Convolutional neural networks has been used in recent years for the analysis of medical image data. CNN's development of its machine learning roots is traced in this study. We also provide a brief mathematical description of CNN as well as the pre-processing process required for medical images before inserting them into CNN. Using CNN in many medical domains, including classification, segmentation, detection, and localization, we evaluate relevant research in the field of medical imaging analysis. It can be concluded that CNN's deep learning view of medical imaging is very helpful for medical parties in their work

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