Translation of Sign Language Using Generic Fourier Descriptor and Nearest Neighbour

Izzah, Abidatul; Suciati, Nanik

doi:10.5121/ijci.2014.3104

Cited by 13 publications

(3 citation statements)

References 5 publications

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“…To realize continuous recognition, there are some works such as the hidden Markov model (HMM) and dynamic time warping (DTW) [14], and methods using Random Forest, artificial neural networks (ANN), and support vector machines (SVM) [15]. To realize non-continuous recognition, there are some studies, such as the k-nearest neighbor (k-NN) method [16], SVM [17], and sparse Bayesian classification of feature vectors generated from motion gradient orientation images extracted from input videos [18]. To realize sign language recognition for non-continuous and non-time-series data, there are some works such as the method of k-NN [19], similarity calculation using Euclidean distance [20], cosine similarity [19][21], ANN [22], SVM [23], and convolutional neural network (CNN) [24].…”

Section: Related Workmentioning

confidence: 99%

A New Global Sign Language Recognition System Utilizing the Editable Mediator: Integration with Local Hand Shape Recognition

Nakanishi,

Minematsu,

Okada

et al. 2024

Frontiers in Artificial Intelligence and Applications

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We introduced a novel approach to global sign language recognition by leveraging the capabilities of the Editable Mediator. Traditional methods have often been limited to recognizing sign languages from specific linguistic regions, necessitating ad hoc implementation for multilingual regions. Our method aims to bridge this gap by providing a unified framework for recognizing sign languages in various linguistic areas and promoting global communication. At the core of our system is the Editable Mediator, a mechanism that determines the actual sign meaning from various local hand-shape recognitions. Instead of focusing on specific sign language notations, such as HamNoSys, our approach emphasizes the recognition of common primitive actions shared across different sign languages. These primitive actions are recognized by multiple modules, and their combinations are interpreted by the Editable Mediator to determine the intended sign-language message. This architecture not only simplifies the recognition process, but also offers flexibility. By merely editing the Editable Mediator, our system can adapt to various sign languages worldwide without the need for extensive retraining or ad hoc implementation. This innovation reduces barriers to introducing new sign language systems and promotes a more inclusive global communication platform.

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Section: Related Workmentioning

confidence: 99%

A New Global Sign Language Recognition System Utilizing the Editable Mediator: Integration with Local Hand Shape Recognition

Nakanishi,

Minematsu,

Okada

et al. 2024

Frontiers in Artificial Intelligence and Applications

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show abstract

“…It may be quite difficult to communicate with those who have hearing impairments. Worldwide, deaf and hard of hearing persons mostly communicate using sign languages [3]. The social connection and communication gap between them and the able-bodied persons can be filled up most powerfully and effectively through sign language.…”

Section: Introductionmentioning

confidence: 99%

Real time sign language detection system using deep learning techniques

2022

pnr

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Humans require communication in order to survive. It is a fundamental and effective method for communicating thoughts, feelings, and points of view. A greater number of babies are being born with hearing abnormalities, which puts them at a communication disadvantage with the rest of the world, according to data on physically challenged children during the last decade. People who are deaf or hard of hearing typically use sign languages to communicate. Hand gestures are used by Deaf or Mute persons to communicate; as a result, non-Deaf people have a hard time understanding their messages. Systems that can detect different signs and provide information to common people are thus necessary. To address this issue, we have developed an automated sign language detection system using deep learning, which helps deaf/mute people can communicate with normal people.

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“…Moreover, gesture recognition using the hand contour faces additional problems; there may be little difference between the contours of different gestures, and relatively high difference between the contours of the same gesture. In the state‐of‐the‐art of the contour based automatic SL recognition, the hand contour has been described by Fourier descriptors [29–32], Hu moments [33–36], wavelets [37–39], shape context [40, 41] and so on [1, 2, 4].…”

Section: Introductionmentioning

confidence: 99%