Indonesian Sign Language Number Recognition using SIFT Algorithm

Mahfudi, Isa; Sarosa, Moechammad; Asmara, Rosa Andrie; Gustalika, M. Azrino

doi:10.1088/1757-899x/336/1/012010

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…To date, a significant amount of research had been done to perform robust detection, matching and recognition of discriminative features points inside an image. Feature detectors and matching algorithms had been developed for various purposes, [8]- [12], features recognition [13]- [16], and gesture recognition [17], among others. This work aims to evaluate the performance of SIFT [18] against common image deformations applied to a clean synthetic mat motif image.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of SIFT against common image deformations on iban plaited mat motif images

Joseph

Hipiny²,

Ujir³

et al. 2021

IJEECS

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Decorative plaited mat is one of the many examples of rich plait work often seen on Borneo handicraft products. The plaited mats are decorated with simple and complex motif designs; each has its own special meaning and taboos. The motif designs are used as a reflection of environment and the traditional beliefs in the Iban community. In line with efforts from UNESCO’s and Sarawak Government’s, digitization, and the use of IR4.0 technologies to preserve and promote this cultural heritage is encouraged. Towards this end goal, we present a novel image dataset containing 10 Iban plaited mat motif classes. The plaited mat motifs are made of diagonal and symmetrical shapes, as well as geometric and non-geometric patterns. Classification’s accuracy using scale-invariant feature transform (SIFT) features was evaluated against 6 common image deformations: zoom+rotation, viewpoint, image blur, JPEG compression, scale and illumination, across multiple threshold values. Varying degrees of each deformation were applied to a digitally cleaned (and cropped) image of each mat motif class. We used RANSAC to remove outliers from the noisy SIFT matching result. The optimal threshold value is 2.0e-2 with a reported 100.0% matching accuracy for the scale change and zoom+rotation set.

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Section: Introductionmentioning

confidence: 99%

Performance evaluation of SIFT against common image deformations on iban plaited mat motif images

Joseph

Hipiny²,

Ujir³

et al. 2021

IJEECS

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show abstract

“…Other studies utilized Hidden Markov Model [13] and Naïve Bayes [14] methods. Meanwhile, [15] used the generalized learning vector quantization model to recognize BISINDO and [16] utilized Scale Invariant Features Transform (SIFT) algorithm to recognize Indonesian Sign Language numbers. Iqbal et al implemented a mobile device using a Discrete Time Warping for recognizing SIBI [17].…”

Section: Introductionmentioning

confidence: 99%

Indonesia Sign Language Recognition using Convolutional Neural Network

Dwijayanti¹,

Hermawati²,

Taqiyyah³

et al. 2021

IJACSA

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“…Although there are many technologies to help people with speech impairments such as hearing aids and cochlear implants, these technologies will not work in a noisy environment. Futhermore, not all normal people understand the sign languange so that the message is not conveyed properly and will lead to misunderstandings [3]. Therefore, an interactive media is needed to bridge the communication between normal and deaf people.…”

Section: Introductionmentioning

confidence: 99%

Developing BacaBicara: An Indonesian Lipreading System as an Independent Communication Learning for the Deaf and Hard-of-Hearing

Muljono

Saraswati

Winarsih

et al. 2019

Int. J. Emerg. Technol. Learn.

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Deaf and hard-of-hearing people have limitations in communication, espe-cially on aspects of language, intelligence, and social adjustment. To com-municate, deaf people use sign language or lipreading. For normal people, it is very difficult to use sign language. They have to memorize many hand signs. Therefore, lipreading is a necessary for communication between nor-mal and deaf people. In Indonesia, there is still few education media for deaf people to learn lipreading. To overcome this challenge, we develop a lipread-ing educational media to help deaf and hard-of-hearing to learn Bahasa In-donesia, called BacaBicara. User-Centered Design (UCD) is implemented to design the application and to analyze the constraints and conceptual models for the needs of users. This conceptual model uses the picture, lipreading video, text, and sign language to help the users understand the contents. The High fidelity prototype was implemented for evaluating usability testing. Based on the evaluation of the application, the results show that the proto-type matches the usability goals and the user experience.

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Indonesian Sign Language Number Recognition using SIFT Algorithm

Cited by 6 publications

References 3 publications

Performance evaluation of SIFT against common image deformations on iban plaited mat motif images

Performance evaluation of SIFT against common image deformations on iban plaited mat motif images

Indonesia Sign Language Recognition using Convolutional Neural Network

Developing BacaBicara: An Indonesian Lipreading System as an Independent Communication Learning for the Deaf and Hard-of-Hearing

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