A Real-Time System for Recognition of American Sign Language by using Deep Learning

Taşkıran, Murat; Kıllıoğlu, Mehmet; Kahraman, Nihan

doi:10.1109/tsp.2018.8441304

Cited by 82 publications

(40 citation statements)

References 8 publications

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“…Similar observations concerning American Sign Language were noted in an experiment by Taskiran et al [116], where a CNN structure was used to extract and classify features obtained from the American Sign Language. The CNN model had the following features: an input layer, a pooling layer, two 2D convolutional layers, two dense layers, and a flattening layer.…”

Section: ) Deep Learning Techniquessupporting

confidence: 75%

“…In addition, methods that rely on sensors or customized input devices were not given proper consideration. Individual ap- Sign Language [13], [39], [44], [52], [72]- [74], [76], [95], [96], [116]- [120], [125], [128], [131], [134] American Sign Language [38] Italian Sign Language [40], [121], [169] Arabic Sign Language [49]- [51], [65], [71], [81], [97] Chinese Sign Language [70] Argentine Sign Language [63], [64] Danish and New Zealand Sign Language [45], [122] Bengali Sign Language [3], [66], [77], [95], [133] German Sign Language [67] Japanese Sign Language [68], [115], [123], [124] Indian Sign Language [69], [130], [132] Indonesian Sign Language [46] Portuguese Sign Language [126] Dutch Sign Language…”

Section: Related Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Al‐Qurishi¹,

Khalid²,

Souissi³

2021

IEEE Access

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People with hearing impairments are found worldwide; therefore, the development of effective local level sign language recognition (SLR) tools is essential. We conducted a comprehensive review of automated sign language recognition based on machine/deep learning methods and techniques published between 2014 and 2021 and concluded that the current methods require conceptual classification to interpret all available data correctly. Thus, we turned our attention to elements that are common to almost all sign language recognition methodologies. This paper discusses their relative strengths and weaknesses, and we propose a general framework for researchers. This study also indicates that input modalities bear great significance in this field; it appears that recognition based on a combination of data sources, including vision-based and sensor-based channels, is superior to a unimodal analysis. In addition, recent advances have allowed researchers to move from simple recognition of sign language characters and words towards the capacity to translate continuous sign language communication with minimal delay. Many of the presented models are relatively effective for a range of tasks, but none currently possess the necessary generalization potential for commercial deployment. However, the pace of research is encouraging, and further progress is expected if specific difficulties are resolved.

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Section: ) Deep Learning Techniquessupporting

confidence: 75%

Section: Related Studiesmentioning

confidence: 99%

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Al‐Qurishi¹,

Khalid²,

Souissi³

2021

IEEE Access

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“…Penelitian yang dilakukan oleh [11] dalam mengenali bahasa isyarat menggunakan deep learning pada gambar bahasa isyarat Amerika yang mencakup 26 huruf dan 10 angka menghasilkan tingkat akurasi sebesar 98.05%.…”

Section: Pendahuluanunclassified

Implementation of Deep Learning on Number Recognition in Sign Language

Celsia

Sandag

2021

JST

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Penderita tuna rungu dan tuna wicara sering merasa kesulitan untuk berkomunikasi dengan masyarakat umum. Era teknologi saat ini memerlukan system cerdas yang dapat membantu komunikasi para penderita tuna rungu dan wicara. Oleh karena itu pada peneltian ini peneliti membuat model dalam klasifikasi Bahasa isyarat menggunakan, Convolutional Neural Network (CNN) dan Artificial Neural Network (ANN). CNN merupakan metode yang banyak digunakan untuk mendeteksi bahasa isyrat, dimana setiap gerakan tangan bahasa isyarat dapat terdeteksi dan memberikan informasi. Hasil penelitian ini diambil dari tiga tahapan masingmasing sebanyak 100 epoch yang dilakukan pada proses train, dimana untuk proses train pertama mendapatkan hasil akurasi 99%, proses train kedua mendapatkan hasil akurasi 100%, dan proses train ketiga mendaptkan hasil akurasi 99%. Kesimpulan penelitian ini adalah pengklasifikasian bahasa isyarat menggunakan metode ANN dan CNN memberikan hasil akurasi yang tinggi. Hasil akurasi dengan rata-rata 94,5% di dapatkan pada penelitian ini, dengan hasil tersebut dapat membantu komunikasi antara penderita tuna rungu, tuna wicara, dan orang normal. Berdasarkan model yang kami buat menggunakan CNN dan ANN, memiliki performa yang baik sebesar 1-3 % lebih baik dengan algoritma yang lain.

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“…Convolutional Neural Networks (CNN) are very popular NN algorithms that are often applied to image classification problems, CNNs have proven to be successful in areas like image classification and recognition [7]. CNN does take the image and pass it through a series of layers.…”

Section: Iiiii the Proposed Recognition Modelmentioning

confidence: 99%

A Saudi Sign Language Recognition System based on Convolutional Neural Networks

Al-Obodi¹,

Al-Hanine²,

Al-Harbi³

et al. 2020

International Journal of Engineering Research and Technology

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Sign language is the main communication method for deaf people. It is a collection of signs that deaf people use to deal with each other. Deaf people find it difficult to communicate with normal people, as most of them do not understand the signs of the sign language. Sign language recognition systems translate the signs into natural languages and thus shorten the gap between deaf and normal people. Many studies have been done on different sign languages. There is a considerable number of studies on the standard Arabic sign language. In Saudi Arabia, deaf people use the Saudi language, which is different from standard Arabic. This study proposes a smart recognition system for Saudi sign language based on convolutional neural networks. The system is based on the Saudi Sign language dictionary, which was published recently in 2018. In this study, we constructed a dataset of 40 Saudi signs with about 700 images for each sign. We then developed a deep convolutional neural network and trained it on the constructed dataset. To have better recognition, we took images of the signs with different hand sizes, skin colors, lighting, backgrounds, and with/without accessories. The results showed that the recognition model achieved an accuracy of 97.69% for training data and 99.47% for testing data. The model was implemented in two versions: mobile and desktop.

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A Real-Time System for Recognition of American Sign Language by using Deep Learning

Cited by 82 publications

References 8 publications

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Implementation of Deep Learning on Number Recognition in Sign Language

A Saudi Sign Language Recognition System based on Convolutional Neural Networks

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