Recognition of hand gestures of English alphabets using HOG method

Viswanathan, Daleesha M.; Idicula, Sumam Mary

doi:10.1109/icdse.2014.6974641

Cited by 4 publications

(2 citation statements)

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“…Histograms of Oriented Gradients are evaluated for feature extraction where K-Nearest Neighbors (KNN) classifier is used for gesture recognition [2]. Convolution neural network is used for gesture recognition in real time [3]. Data augmentations like zooming, shearing, rotation are applied on datasets before giving it to CNN for better accuracy [4].…”

Section: Related Workmentioning

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: Related Workmentioning

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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“…Penelitian terkait dengan ASL telah banyak diterapkan dengan berbagai macam metode klasifikasi dan ekstraksi fitur [3] [4]. Berdasarkan penelitian [5] [6] menunjukkan bahwa hasil akurasi yang diperoleh dengan menggunakan klasifikasi k-Nearest Neighbor (k-NN) serta ekstraksi fitur Histogram of Oriented Gradient (HOG) ataupun Linear Discriminant Analysis (LDA), yaitu sebesar 98,33% dan 96%. Pada penelitian [7] citra ASL diekstraksi fiturnya menggunakan HOG lalu menggunakan k-NN sebagai klasifikasinya.…”

Section: Pendahuluanunclassified

Implementasi LDA pada fitur HOG untuk Klasifikasi ASL Menggunakan K-NN

Rivan¹,

Irsyad²,

Kevin³

et al. 2020

JATISI

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Sign language merupakan suatu cara alternatif yang bisa digunakan untuk berkomunikasi dengan menggunakan isyarat, salah satu jenisnya yaitu American Sign Language (ASL). Dataset sign language yang digunakan yaitu dalam bentuk dataset citra yang diproses menggunakan ekstraksi fitur Histogram of Oriented Gradients (HOG) dan selanjutnya direduksi menggunakan Linear Discriminant Analsysis (LDA). Selanjutnya hasil reduksi digunakan untuk klasifikasi K-Nearest Neighbors (k-NN). Tiga jenis distance yang digunakan yaitu euclidean, manhattan dan chebyshev. Hasil terbaik diperoleh menggunakan manhattan distance dengan nilai K = 3 dengan presisi sebesar 72,42 %.

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