An American Sign Language detection system using HSV color model and edge detection

Konwar, A. Sharmila; Borah, Bhargab; Tuithung, C. T.

doi:10.1109/iccsp.2014.6949942

Cited by 25 publications

(6 citation statements)

References 10 publications

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“…Feature vector of every image which was pre-processed is extracted, by using the designed CNN architecture. Mean-subtracted images are used for feature extraction, which we will get by subtraction of mean-images from the training or testing images [12].…”

Section: Feature Extractionmentioning

confidence: 99%

Hand Gesture recognition using Deep Learning

Sangeetha

Srivathsan

Dinesh

et al. 2023

2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS)

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Hearing impaired individuals use sign languages to communicate with others within the community. Because of the wide spread use of this language, hard-of-hearing individuals can easily understand it but it is not known by a lot of normal people. In this paper a hand gesture recognition system has been developed to overcome this problem, for those who don't recognize sign language to communicate simply with hard-of-hearing individuals. In this paper a computer vision-based system is designed to detect sign Language. Datasets used in this paper are binary images. These images are given to the convolution neural network (CNN). This model extracts the features of the image and classifies the images, and it recognises the gestures. The gestures used in this paper are of American Sign Language. In real time system the images are converted to binary images using Hue, Saturation, and Value (HSV) colour model. In this model 87.5% of data is used for training and 12.5% of data is used for testing and the accuracy obtained with this model is 97%.

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Section: Feature Extractionmentioning

confidence: 99%

Hand Gesture recognition using Deep Learning

Sangeetha

Srivathsan

Dinesh

et al. 2023

2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS)

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“…In 2014, an ASL detection system has been designed [17] by detecting human skin color using HSV color model and edge detection technique with morphological operations. A total of 100 gestures are tested and 65% of the total gestures were recognized properly.…”

Section: Related Workmentioning

confidence: 99%

“…This color space allows person to specify the boundary of the skin pixels only in terms of hue and saturation. Illumination (V) provides brightness facts, it is usually ignored to lessen illumination dependency of skin color [17]. Therefore, in this proposed work, HSV color model is used to detect the skin region and mark the skin pixels in signed input gesture image.…”

Section: A Hsv Color Modelmentioning

confidence: 99%

“…14 clearly highlights the proposed ASL alphabets gestures recognition method yields the better ASL alphabets gestures recognition rate of 93.05% comparing with all the other existing traditional ASL alphabets gestures recognition techniques. It is also noticed that the proposed ASL alphabets gestures recognition method shows the huge recognition rate difference with the ASL alphabets gestures recognition technique with recognition rate of 65% which is implemented using HSV color Model and Edge detection system [17], and also with the ASL alphabets gestures recognition technique with the recognition rate of 71% which is implemented using Gaussian and Canny Filters with flood fill algorithm [23]. Following are the various screen shots obtained from the proposed algorithm for non-occluded gestures of ASL Numbers.…”

Section: Fig10 Asl Alphabet (Occluded) Recognition Processmentioning

confidence: 99%

See 1 more Smart Citation

American Sign Language Recognition System: An Optimal Approach

Shivashankara¹,

Srinath²

2018

IJIGSP

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The Sign language is a visual language used by the people with the speech and hearing disabilities for communication in their daily conversation activities. It is completely an optical communication language through its native grammar, be unlike fundamentally from that of oral languages. In this research paper, presented an optimal approach, whose major objective is to accomplish the transliteration of 24 static sign language alphabets and numbers of American Sign Language into humanoid or machine decipherable English manuscript. Pre-processing operations of the signed input gesture are done in the first phase. In the next phase, the various region properties of pre-processed gesture image is computed. In the final phase, based on the properties calculated of earlier phase, the transliteration of signed gesture into text has been carried out. This paper also presents the statistical result evaluation with the comparative graphical depiction of existing techniques and proposed technique.

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“…Hal yang sama yang dilakukan pada warna latar belakang, menghasilkan gambar biner yang lain. Operasi Logis "dan" kemudian dilakukan pada dua gambar biner dan hasilnya adalah kabur, melebar terkikis untuk menghilangkan suara dan gambar biner akhir diperoleh, seperti yang ditunjukkan pada gambar di atas [4].…”

Section: A Presampling Dan Segementasi Tanganunclassified

Penerjemahan Bahasa Isyarat Menggunakan Kamera pada Telepon Genggam Android

Andrian

Purwanto

Mardiyanto

2016

JTITS

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A221Abstrak-Penginderaan visual atau machine vision merupakan suatu proses manipulasi data citra. Data tersebut dapat digunakan untuk melakukan intepretasi banyak hal, salah satunya yaitu pengenalan gesture. Pengenalan gesture adalah antarmuka yang dapat mengenali gerak-isyarat seorang manusia dan mentranslasikan gerakan tersebut sebagai instruksi yang dapat dipahami oleh komputer. Pengenalan gesture dapat digunakan untuk penerjemahkan bahasa isyarat pada orang tunawicara. Hal ini karena banyaknya orang yang tidak mengerti bahasa tangan orang tunawicara. Sehingga, orang tunawicara kesulitan dalam berinteraksi di masyarakat.Pada tugas akhir ini pengenalan gesture untuk penerjemahan bahasa isyarat lebih mengarah pada hand recognition, yaitu pendeteksian perubahan gerak tangan, dengan menggunakan android mobile phone sebagai divaisnya. Android mobile phone memiliki kamera untuk menangkap citra orang tuna wicara saat berkomunikasi menggunakan bahasa isyarat berupa gerakan tangan. Selanjutnya, citra diproses oleh processing unit android untuk melakukan proses hand recognition. Setelah proses tersebut selesai, maka layar display akan memunculkan huruf atau kata dari perubahan posisi gerak tangan yang dilakukan orang tunawicara yang berada di depan kamera.Kata kunci-pengenalan gesture, kamera, machine vision, android, hand recognition.

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An American Sign Language detection system using HSV color model and edge detection

Cited by 25 publications

References 10 publications

Hand Gesture recognition using Deep Learning

Hand Gesture recognition using Deep Learning

American Sign Language Recognition System: An Optimal Approach

Penerjemahan Bahasa Isyarat Menggunakan Kamera pada Telepon Genggam Android

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