An XML-gloss annotation system for sign language processing

Othman, Achraf; Jemni, Mohamed

doi:10.1109/icta.2017.8336054

Cited by 5 publications

(6 citation statements)

References 8 publications

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“…Othman et al [10] presented the transcription system which annotates the American sign language. They show that their system would be helpful in machine translation in order to use it in differnet fields.…”

Section: Literature Reviewmentioning

confidence: 99%

A Real-Time Automatic Translation of Text to Sign Language

Sanaullah¹,

Ahmad²,

Kashif³

et al. 2022

Computers, Materials &Amp; Continua

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Communication is a basic need of every human being; by this, they can learn, express their feelings and exchange their ideas, but deaf people cannot listen and speak. For communication, they use various hands gestures, also known as Sign Language (SL), which they learn from special schools. As normal people have not taken SL classes; therefore, they are unable to perform signs of daily routine sentences (e.g., what are the specifications of this mobile phone?). A technological solution can facilitate in overcoming this communication gap by which normal people can communicate with deaf people. This paper presents an architecture for an application named Sign4PSL that translates the sentences to Pakistan Sign Language (PSL) for deaf people with visual representation using virtual signing character. This research aims to develop a generic independent application that is lightweight and reusable on any platform, including web and mobile, with an ability to perform offline text translation. The Sign4PSL relies on a knowledge base that stores both corpus of PSL Words and their coded form in the notation system. Sign4PSL takes English language text as an input, performs the translation to PSL through sign language notation and displays gestures to the user using virtual character. The system is tested on deaf students at a special school. The results have shown that the students were able to understand the story presented to them appropriately.

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Section: Literature Reviewmentioning

confidence: 99%

A Real-Time Automatic Translation of Text to Sign Language

Sanaullah¹,

Ahmad²,

Kashif³

et al. 2022

Computers, Materials &Amp; Continua

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“…They are constantly used throughout our daily lives whether at school, work, or home they play a vital role in ensuring that our emotions are correctly understood by the person we're interacting with. Facial expressions are one of the most effective methods to convey emotions to another human being [1].…”

Section: Introductionmentioning

confidence: 99%

FER_ML: Facial Emotion Recognition using Machine Learning

AbdElminaam

Mostafa

Ghareeb

et al. 2023

Journal of Computing and Communication

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Recently, facial recognition has been one of the most crucial technologies people need. Facial recognition has attracted a lot of the crowd; for example, it has been used in security on most modern devices. Using machine and deep learning, overall performance will be improved, and the identification accuracy will be more precise. We aim to discover how well these algorithms perform in classifying human facial expressions and whether or not we can depend on them. The steps are as follows. First, we embed the images from the dataset, then split the dataset into 70% training data and 30% testing data; after that, we apply five different algorithms: Support Vector Machine, K-nearest Neighbor, Logistic Regression, Naive Bayes, and Random Forest. Support Vector Machine achieved an accuracy rate of 36%, K-nearest Neighbor achieved an accuracy rate of 52.3%, Logistic regression achieved an accuracy rate of 64.2%, and Naive Bayes achieved an accuracy rate of 38.1%. Random Forest achieved an accuracy rate of 51.7%. The dataset used was a cleaned version of the FER13 dataset, which contains 16,780 images divided into five classes (angry, happy, neutral, disgust, and fear). The results show that Logistic Regression proved to be the most accurate classifier among the presented ones, with an F1-Score of 63.8% and an accuracy of 64.2%.

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“…Sign languages are visual-gesture-based languages regarded as the deaf community's mainstream language. Gestures and visual channels communicate in this language [1]. Hand gestures, body movements, and facial expressions are utilized for communicating in sign language.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Gloss Sign Language Translation using Transformer

Mohamed

Hefny

Amin

2022

Journal of Computing and Communication

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One of the most recent applications of machine learning is to translate sign language into natural language. Many studies have attempted to classify sign language based on whether it is gesture or facial expression. These efforts, however, ignore genuine sentences' linguistic structure and context. The quality of traditional translation methods is poor, and their underlying models are not salable. They also take a long time to complete. The contribution of this paper is that it suggests utilizing a transformer to perform bidirectional translation using a deep learning approach. The proposed models experiment on the ASLG-PC12 corpus. The experimental results reveal that the proposed models outperform other approaches to the same corpus in both directions of translation, with ROUGE and BLEU scores of 98.78% and 96.89%, respectively, when translating from text to gloss. Additionally, the results indicate that the model with two layers achieves the best result with ROUGE and BLEU scores of 96.90% and 84.82% when translating from gloss to text.

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An XML-gloss annotation system for sign language processing

Cited by 5 publications

References 8 publications

A Real-Time Automatic Translation of Text to Sign Language

A Real-Time Automatic Translation of Text to Sign Language

FER_ML: Facial Emotion Recognition using Machine Learning

A Deep Learning Approach for Gloss Sign Language Translation using Transformer

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