Sign Language Translator Application Using OpenCV

Triyono, Liliek; Pratisto, Eko Harry; Bawono, Sahirul Alim Tri; Purnomo, Fendi Aji; Yudhanto, Yudho; Raharjo, B

doi:10.1088/1757-899x/333/1/012109

Cited by 10 publications

(3 citation statements)

References 3 publications

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“…The adapted computational framework is computer vision [41]; it is an interdisciplinary field of science, technology and engineering based on image processing technique [42] where a digital image and video are interpreted and analysed via executed programmed command. Computer vision based image processing technique can be executed in several platform namely Matlab (Matrix Labatory) [43], [44], OpenCV (Opensource computer vision) [45], [46], CUDA [47], [48], TensorFlow [49], [50] and SimpleCV [51]. Despite the fact that there are several software for image processing applications, Matlab is widely recommended and prominent among researchers because of its stable, extensive and strong mathematical and numerical toolbox support for the execution of advanced and complex algorithms.…”

Section: Image Processing Via Opencvmentioning

confidence: 99%

Alternative Method of Nature Inspired Geometrical Design Strategy for Drag Induced Wind Turbine Blade Morphology

Sanderasagran

Aziz

Oumer

et al. 2022

Int. J. Automot. Mech. Eng.

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Although drag driven wind turbine is regarded as an efficient rotor for low wind speed region, design reconfiguration is a continuous process in order to improve the performance of the rotor. The main governing factor that influences the performance of the rotor is the blade morphology. Hence, this paper presents a proposed nature inspired design approach for the development of drag driven wind turbine blade morphology. The design approach framework comprise of 3 main elements namely image processing, geometrical analysis and bio-hybridization. The proposed bio-hybridized design consist of blade mainframe curve inspired by nautilus shell and barnacle on the blade surface. It is found that integration of barnacle geometries on the surface of the blade has affected the performance of the rotor. Result shows that the peak Cm is at λ = 0.55 for experimental and CFD is Cm = 0.238 and Cm = 0.253 respectively. The proposed design resulted in experimental and numerical Cp = 0.113 and Cp = 0.127 respectively at 7 m/s and λ = 0.7. The presented design technique with appropriate design bio-element provides a systematic method for engineers to model wind turbine blade morphologies.

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Section: Image Processing Via Opencvmentioning

confidence: 99%

Alternative Method of Nature Inspired Geometrical Design Strategy for Drag Induced Wind Turbine Blade Morphology

Sanderasagran

Aziz

Oumer

et al. 2022

Int. J. Automot. Mech. Eng.

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show abstract

“…During our research process, we found that Tryono et al have used the OpenCV library and Support Machine Learning to build an Android application to interpret sign language [27]. In 2019, a research team used computer vision and deep neural networks to capture hand sign digits and translate them to Bangla [2].…”

Section: Overview Of Python Frameworkmentioning

confidence: 99%

“…As mentioned before, Tryono et al have utilized the OpenCV library and Support Machine Learning to build an Android mobile application to translate sign language [27]. They examined their app with two different light settings Manuscript submitted to ACM…”

Section: Overview Of Python Frameworkmentioning

confidence: 99%

Median American Sign Language Interpretation Software

Anderson¹,

Casad²,

Cathcart³

et al. 2022

Preprint

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The Median American Sign Language Interpretation Software (ASL) Interpretation Software is a web application that is capable of interpreting American Sign Language in real-time, utilizing an internet connection and a primary web camera, complete with basic phrases and letters. Extensive use of Deep Learning and Neural Networks, specifically Convoluted Neural Networks, enables Median to interpret video inputs and generate accurate results directly displayed to the user in text format. The ultimate goal for Median is to have it act as a bridge between hearing people and members of the deaf community, allowing deaf people to communicate with non-signing people using American Sign Language. Furthermore, Median has been designed to benefit people who lack access to a human ASL Translator, as its format as a website allows it to be accessible anywhere at any time, giving increased availability over human interpreters. Median is designed to be a very versatile program with great potential for growth and expansion.

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An improved custom convolutional neural network based hand sign recognition using machine learning algorithm

Moon,

Yenurkar,

Nyangaresi

et al. 2024

Engineering Reports

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The biggest challenge the deaf and dumb group faces is that individuals around them do not understand sign language, which they use to communicate with one another. Written communication is slower than face‐to‐face contact, despite the fact that it can be used. Many sign languages have been developed around the world because they are more effective in emergency situations than text‐based communication. India in‐spite of having the large deaf population of almost 18 million and having only around 250 trained/untrained; skilled interpreters. The proposed system can utilize a custom convolution neural networks (CCNNs) model to identify hand motions in order to resolve this issue. This system uses a filter to process the hand before sending it through a classifier to identify the type of hand movements. CCNN strategy employs two levels of algorithm to predict and evaluate symbols that are increasingly similar to one another in order to get as close to precisely recognizing the symbol presented as possible. Convolutional neural networks (CNNs) are able to precisely identify a variety of gestures after being trained on large datasets of hand sign photographs. As a result of their frequent usage of many layers of filters and pooling to extract relevant information from the input images, these networks can recognize hand signs with an accuracy rate of 99.95%, which is much greater than previously built models like SIGNGRAPH, SVM, KNN, CNN + Bi‐LSTM, 3D‐CNN and 2D CNN network and 1D CNN skeleton network. The simulation result shows that a suggested CCNN‐based learning approach is useful for hand sign detection and future usage research when compared with existing machine learning models.

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Sign Language Translator Application Using OpenCV

Cited by 10 publications

References 3 publications

Alternative Method of Nature Inspired Geometrical Design Strategy for Drag Induced Wind Turbine Blade Morphology

Alternative Method of Nature Inspired Geometrical Design Strategy for Drag Induced Wind Turbine Blade Morphology

Median American Sign Language Interpretation Software

An improved custom convolutional neural network based hand sign recognition using machine learning algorithm

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