Classification of hand gesture in Indonesian sign language system using Naive Bayes

Pramunanto, Eko; Sumpeno, Surya; Legowo, Rafiidha Selyna

doi:10.1109/issimm.2017.8124288

Cited by 10 publications

(5 citation statements)

References 3 publications

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“…There are other taxonomies that are often mentioned, such as artificial neural networks (ANNs) [51], Nave Bayes classifier (NBC) [52], Relevance Vector Machine (RVM) [53], etc. Artificial intelligence refers to the capability to simulate, extend, and enhance human intelligence through intelligent systems.…”

Section: Other Classification Approachesmentioning

confidence: 99%

A Survey on Chinese Sign Language Recognition: From Traditional Methods to Artificial Intelligence

Jiang,

Zhang,

Lei

et al. 2024

CMES

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Research on Chinese Sign Language (CSL) provides convenience and support for individuals with hearing impairments to communicate and integrate into society. This article reviews the relevant literature on Chinese Sign Language Recognition (CSLR) in the past 20 years. Hidden Markov Models (HMM), Support Vector Machines (SVM), and Dynamic Time Warping (DTW) were found to be the most commonly employed technologies among traditional identification methods. Benefiting from the rapid development of computer vision and artificial intelligence technology, Convolutional Neural Networks (CNN), 3D-CNN, YOLO, Capsule Network (CapsNet) and various deep neural networks have sprung up. Deep Neural Networks (DNNs) and their derived models are integral to modern artificial intelligence recognition methods. In addition, technologies that were widely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods. Sign language data collection includes acquiring data from data gloves, data sensors (such as Kinect, Leap Motion, etc.), and high-definition photography. Meanwhile, facial expression recognition, complex background processing, and 3D sign language recognition have also attracted research interests among scholars. Due to the uniqueness and complexity of Chinese sign language, accuracy, robustness, real-time performance, and user independence are significant challenges for future sign language recognition research. Additionally, suitable datasets and evaluation criteria are also worth pursuing.

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Section: Other Classification Approachesmentioning

confidence: 99%

A Survey on Chinese Sign Language Recognition: From Traditional Methods to Artificial Intelligence

Jiang,

Zhang,

Lei

et al. 2024

CMES

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“…BISINDO merupakan bahasa isyarat yang secara alamiah lahir dengan mengadopsi nilai budaya asli Indonesia dan lebih mudah dipergunakan di kehidupan sehari-hari oleh kalangan tunarungu [2]. SIBI merupakan bahasa isyarat yang mengacu pada American Sign Language (ASL) dan telah digunakan secara luas [3]. SIBI merupakan bahasa isyarat yang diadaptasi dari bahasa lisan, yang sudah ditetapkan oleh pemerintah melalui Keputusan Mendikbud RI Nomor 0161/U/1994 dan telah dipergunakan di SLB yang ada di Indonesia [4].…”

Section: Iunclassified

Pengenalan Alfabet SIBI Menggunakan Convolutional Neural Network sebagai Media Pembelajaran Bagi Masyarakat Umum

Fadhilah,

Marpaung

2023

JPIT

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SIBI is one of the Sign Languages used in Indonesia and has been widely used in the community, especially the school (SLB). Communication limitations of the deaf and speech community cause limited communication with the general public, especially many general public who do not know Sign Language or SIBI. For this reason, this research was conducted in order to become a learning media for the general public in recognizing the SIBI alphabet so that it can support communication with the deaf and speech community. This research was conducted to become a medium that can be used as a learning medium in the introduction of the SIBI alphabet. The method used in this research is CNN. CNN is used because it is a deep learning method that has the most significant results in image recognition. The data used is 2,600 images which are divided into 80% training data and 20% validation data. Training was done ten times by comparing the parameters that produce the best accuracy. The parameters used are batch size and epoch. From ten trials, the best accuracy is obtained using batch size 8 and epoch 50. The best accuracy produced is 85% training accuracy and 87% validation accuracy.

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“…Leap Motion juga digunakan pada penelitian Pramunanto dkk untuk memberikan pelatihan bahasa isyarat bagi orang normal agar dapat mengatasi hambatan dalam berkomunikasi dengan orang penyandang cacat pendengaran. Fitur Leap Motion yang digunakan adalah sudut antara jari dengan akurasi pengujian sebesar 70,69% [2]. Pada penelitian yang dilakukan oleh Insani dkk menggunakan fitur yang ada pada Leap Motion untuk mengenali huruf alphabet bahasa isyarat pada kondisi pencahayaan redup maupun terang mendapatkan akurasi tertinggi pada cahaya terang 96,2%.…”

Section: Pendahuluanunclassified

Deteksi Gerakan Bahasa Isyarat Menggunakan Euclidean Distance

Insani,

Arifin,

Rasyid

2023

Informatik : Jurnal Ilmu Komputer

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Orang yang memiliki keterbatasan pendengaran/wicara akan sulit berkomunikasi secara lisan dengan orang normal. Cara berkomunikasinya dapat menggunakan tulisan yang lambat dan tidak efisien atau dengan bahasa isyarat. Bahasa isyarat menjadi satu-satunya metode yang efisien digunakan bagi orang dengan keterbatasan pendengaran/wicara. Sedangkan kebanyakan masyarakat normal tidak dapat menggunakan bahasa isyarat. Sehingga sistem untuk mendeteksi bahasa isyarat menjadi sebuah kebutuhan untuk membantu masyarakat dengan keterbatatasan komunikasi secara lisan. Penelitian ini bertujuan untuk mendeteksi bahasa isyarat kedalam teks sesuai dengan makna yang sebenarnya. Pengambilan data dilakukan menggunakan Leap Motion dengan 15 data gerakan isyarat tangan yang memiliki gerakan dasar dan kemiripan dalam gerakannya. Data yang digunakan dalam penelitian ini berupa isyarat abjad A, B, W, M, N, J, Z dan isyarat kata dia, pakai, saya, kakak, adik bingung, kecewa, hai. Metode yang digunakan pada penelitian ini adalah euclidean distance untuk mencari jarak minimum dari nilai support vector yang terdeteksi sama. Inputan pada proses uji adalah huruf W yang dilakukan secara realtime memiliki kemiripan gerakan isyarat dengan huruf B dan kata kecewa nilai Euclidean distance yaitu 0.5475649269013902 maka inputan tersebut dapat terdeteksi sesuai dengan makna yang sebenarnya. Hasil akurasi rata-rata yang didaparkan dari keseluruhan tahap pengujian 15 data gerakan sebesar 88,7%.

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Classification of hand gesture in Indonesian sign language system using Naive Bayes

Cited by 10 publications

References 3 publications

A Survey on Chinese Sign Language Recognition: From Traditional Methods to Artificial Intelligence

A Survey on Chinese Sign Language Recognition: From Traditional Methods to Artificial Intelligence

Pengenalan Alfabet SIBI Menggunakan Convolutional Neural Network sebagai Media Pembelajaran Bagi Masyarakat Umum

Deteksi Gerakan Bahasa Isyarat Menggunakan Euclidean Distance

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