Arabic Sign Language (ArSL) Recognition System Using HMM

Youssif, Aliaa A. A.; Aboutabl, Amal Elsayed; Ali, Hashim

doi:10.14569/ijacsa.2011.021108

Cited by 29 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The comparisons focused on the alphabet and the CNN model. In Table IV, observes and compares related works that used the camera as an input device [11,14] with different machine learining techniques, the proposed system; it should be noted that the accuracy of the suggested approach is significantly greater. This is due to the employment of appropriate data processing techniques in the proposed system.…”

Section: B Comparison With Alternative Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

2023

IJCCCE

View full text Add to dashboard Cite

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.

show abstract

Section: B Comparison With Alternative Methodsmentioning

confidence: 99%

“…In [11], Youssif et al introduced an automatic Arabic Sign Language (ArSL) recognition system based on the dataset containing 20 isolated words from the Standard Arabic sign language. The hand was represented by a model consisting of the palm, the five fingers as ridges at finer scales and the fingertips as even finer scale blobs.…”

Section: Related Workmentioning

confidence: 99%

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

2023

IJCCCE

View full text Add to dashboard Cite

show abstract

“…Youssif et al [45] introduce an automatic ArSL recognition system using HMMs. Twenty isolated words from the standard ArSL have been recognized using a large sample set.…”

Section: Isolated-words Arsl Recognitionmentioning

confidence: 99%

Untitled

2024

INDJCSE

View full text Add to dashboard Cite

Deaf individuals use sign language to interact with each other and with people in general. In most cases, an interpreter is needed in order for a hearing person to have a conversation with a deaf person. The study of recognizing visual signs is becoming more prevalent in the field of computer vision. A deaf person may be able to communicate with hearing people without an interpreter if reliable sign language recognition technology is made available to them. It can be used to generate voice or text, giving the mute greater independence. The study of Arabic sign language recognition is crucial. It facilitates better communication between the deaf and others in the community. This paper attempts to investigate the current Arabic sign language (ArSL) recognition approaches which are commonly and efficiently used for building the automatic translation and recognition of ArSL systems. Furthermore, the focus is given on highlighting the challenges and future work directions to explore the unsolved problems related to ArSL recognition systems.

show abstract

“…To bridge this gap, a system that can translate sign language to words and vice versa is necessary. In (Youssif, A. A., et al, 2011), the authors designed an ArSL recognition system to recognize the Arabic alphabet in a sequence order.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Kurdish Sign Language Recognition Through Random Forest Classifier and Noise Reduction via Singular Value Decomposition (Svd)

Ahmed,

Mahmood,

Mahmood

et al. 2024

SJUOZ

View full text Add to dashboard Cite

Deaf people around the world face difficulty communicating with others. Hence, they use their own language to communicate with each other. This paper introduces a new approach for Kurdish sign language recognition using the random forest classifier algorithm aiming to facilitate communication for deaf communities to communicate with others without relying on human interpreters. On the other side, for further enhancement of the images captured during recognition linear algebra techniques have been used such as singular value decomposition for image compression and Moore–Penrose inverse for blur removal. Kurdish language has 34 alphabets and (10 numeric numbers 10, . . . ,3 ,2 ,1). Additionally, three extra signs have been created and added to the dataset, such as space, backspace, and delete sentences for the purpose of real-time translation. A collection of 800 images has been gathered for each character, out of 800 images, only 80 per character were used due to their similar positions but varied alignment, totalling 3,520 images for the dataset (44 characters 80 images each). Two simulation scenarios were carried out: one with optimal conditions - a white background and adequate lighting, and another with challenges such as complex backgrounds and varied lighting angles. Both achieved high match rates of 96% and 87%, respectively. Further, a classification report analyzed precision, recall, and F1 score metrics.

show abstract

Arabic Sign Language (ArSL) Recognition System Using HMM

Cited by 29 publications

References 22 publications

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

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

Untitled

Enhancing Kurdish Sign Language Recognition Through Random Forest Classifier and Noise Reduction via Singular Value Decomposition (Svd)

Contact Info

Product

Resources

About