Automated Hand Gesture Recognition using a Deep Convolutional Neural Network model

Dhall, Ishika; Vashisth, Shubham; Aggarwal, Garima

doi:10.1109/confluence47617.2020.9057853

Cited by 23 publications

(7 citation statements)

References 9 publications

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“…It reasonable for image and VDO recognition [27]. CNN was developed from multilayer perceptron [28] and applied by down-size data dimension to get rid of unnecessary parameters and down-scale artificial neural network. Additionally, CNN reduces total training time which uses less resource in processing.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…This research base on Kit-A02 is created to extract feature of TSL by 2D-Convolutional Neural Networks (TSR-2DCNN). The researcher found that there is many research in TSL and its application such as skin detection [25], principal component analysis (PCA) [15], one-shot learning hand gesture recognition [16], artificial neural network (ANN) [26], multilayer perceptron (MLP) [22], hidden makov model (HMV) [23] and the most popular is convolutional neural network [17].…”

Section: Introductionmentioning

confidence: 99%

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The Performance of Thai Sign Language Recognition with 2D Convolutional Neural Network Based on NVIDIA Jetson Nano Developer Kit

Gedkhaw¹

2022

TEM Journal

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Thai Sign Language Recognition is a Thai Sign Language learning computer recognition. The system constructs an architecture of T-SLR by TSR- 2DCNN based on NVIDIA Jetson Nano Developer Kit. It is a novelty of automatic translation TSL innovation and reveals the performance of feature extraction and classification to reduce crashed system, overloaded or automatic reboot while complicated processing occurs. The dataset contains 7 gestures in TSL, training images are 7,000 images and validation images are 700 images. The result compares with many techniques as shown that TSR-2DCNN can increase the performance of TSLR in real-time, effectiveness with an accuracy of 0.9914 and loss of 0.03537.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Performance of Thai Sign Language Recognition with 2D Convolutional Neural Network Based on NVIDIA Jetson Nano Developer Kit

Gedkhaw¹

2022

TEM Journal

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show abstract

“…In this paper, the author used a CNN, which has specific layers such as the input layer and output layer, and in between them, there are some hidden layers. The first hidden layer is the convolutional layer, which detects and extracts features from images, then the Max pooling layer is used for dimensionality reduction [5].…”

Section: Literature Reviewmentioning

confidence: 99%

Presentation Slide Control Based on Hand Gestures

Sinaga¹,

Baharuddin²,

Fibriasari³

et al. 2022

IJCATR

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In today's digital world, presentation using a slideshow is an effective and attractive way that helps speakers to convey information and convince the audience. There are ways to control slides with devices like a mouse, keyboard, or laser pointer. The drawback is that one should have previous knowledge about the devices to manage them. Gesture recognition acquired importance a couple of years prior and is utilized to control applications like media players, robot control, and gaming. The hand gesture recognition system is built with gloves and markers. However, using such gloves or tags expands the expense of the system. An Artificial intelligence-based hand gesture detection methodology is proposed in this proposed system. By hand gestures, users can change the presentation slides in forward and backward directions. The use of hand gestures causes the connection simple and helpful and does not need any additional gadgets. The suggested method is to help speakers with a product presentation with naturally improved communication with the computer. Specifically, the proposed system is more viable than a laser pointer since the hand is more apparent and thus can better grab the audience's attention.

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“…In this paper author used a CNN which has specific layers such as input layer, output layer and inbetween them there are some hidden layers. First hidden layer is convolutional layer which detects and extracts features from images, then Max pooling layer used for dimensionality reduction [5].…”

Section: Literature Surveymentioning

confidence: 99%

Automated Digital Presentation Control using Hand Gesture Technique

Powar,

Kadam,

Malage

et al. 2022

ITM Web Conf.

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In today’s digital world Presentation using a slideshow is an effective and attractive way that helps speakers to convey information and convince the audience. There are ways to control slides with devices like mouse, keyboard, or laser pointer, etc. The drawback is one should have previous knowledge about the devices in order to manage them. Gesture recognition has acquired importance a couple of years prior and are utilized to control applications like media players, robot control, gaming. The hand gesture recognition system builds the use of gloves, markers and so on However, the utilization of such gloves or markers expands the expense of the system. In this proposed system, Artificial intelligence-based hand gesture detection methodology is proposed. Users will be able to change the slides of the presentation in both forward and backward directions by just doing hand gestures. Use of hand gestures cause connection simple, helpful, and doesn’t need any additional gadget. The suggested method is to help speakers for a productive presentation with natural improved communication with the computer. Specifically, the proposed system is more viable than utilizing a laser pointer since the hand is more apparent and thus can better grab the attention of the audience.

show abstract

Automated Hand Gesture Recognition using a Deep Convolutional Neural Network model

Cited by 23 publications

References 9 publications

The Performance of Thai Sign Language Recognition with 2D Convolutional Neural Network Based on NVIDIA Jetson Nano Developer Kit

The Performance of Thai Sign Language Recognition with 2D Convolutional Neural Network Based on NVIDIA Jetson Nano Developer Kit

Presentation Slide Control Based on Hand Gestures

Automated Digital Presentation Control using Hand Gesture Technique

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