Indonesia Sign Language Recognition using Convolutional Neural Network

Dwijayanti, Suci; Hermawati, -; Taqiyyah, Sahirah Inas; Hikmarika, Hera; Suprapto, Bhakti Yudho

doi:10.14569/ijacsa.2021.0121046

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…Convolution can be used for a variety of tasks, such as edge detection, image sharpening, blurring, and feature extraction. It is also an essential component of deep learning neural networks, where it is used to extract features from images or other data types [9][10][11][12][13][14][15].…”

Section: Figure 2 Proposed Architecturementioning

confidence: 99%

A Deep Learning-Based Approach for Hand Sign Recognition Using CNN Architecture

Parashar,

Thakur,

Raju

et al. 2023

RIA

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The domain of hand sign recognition, an integral facet of computer vision, encompasses a wide array of practical applications, ranging from interpreting sign language and recognizing gestures to facilitating human-computer interaction. This research elucidates the introduction of a Convolutional Neural Network (CNN) model tailored to the identification of hand signs representing the English alphabet. For model training and validation, a dataset comprising 26,000 grayscale images of hand signs was employed. The model architecture embraced a profound CNN design, featuring numerous layers for convolution and pooling, followed by fully connected layers. Employing the Adam optimizer, the training procedure yielded an impressive accuracy of 96.7% when evaluated on the Kaggle dataset. These outcomes underscore the effectiveness of the proposed CNN model in precisely discerning hand signs corresponding to the English alphabet. The model's potential utility extends to the recognition of intricate manual gestures and realtime applications, including aiding individuals with motor impairments and enriching virtual reality experiences. Hence, this study accentuates the capacity of deep learning to propel the domain of hand sign recognition forward.

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Section: Figure 2 Proposed Architecturementioning

confidence: 99%

A Deep Learning-Based Approach for Hand Sign Recognition Using CNN Architecture

Parashar,

Thakur,

Raju

et al. 2023

RIA

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“…Nilai akurasi yang diperoleh sekitar 98,3%. Walaupun hasil akurasi bagus, namun citra yang digunakan haruslah menggunakan background hijau, sehingga kurang begitu bagus untuk realisasi penggunaanya [5]. Selanjutnya penelitian rujuakan dari Vinna dkk, yang menggunakan model ResNet50V2 memiliki hasil testing yang sangat bagus yakni 100%.…”

Section: Pendahuluanunclassified

SIBI (Sistem Bahasa Isyarat Indonesia) berbasis Machine Learning dan Computer Vision untuk Membantu Komunikasi Tuna Rungu dan Tuna Wicara

Budiman,

Lestanti,

Yuana

et al. 2023

JTMI

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The Indonesian Sign Language System (SIBI) is used to translate sign language into text or speech. SIBI helps improve communication between people using sign language and those who do not understand it. Unlike commonly used languages, SIBI sign language is less known to most people due to a lack of interest. To address this, an artificial intelligence-based application was developed, focusing on deep learning to recognize SIBI sign language hand movements in real-time. The model was created with 20 epochs, a batch size of 16, and a learning rate of 0.001. It consists of 13 layers, with the ReLU activation function used for the input layer, while the output layer uses Sigmoid. The ADAM optimizer was used to expedite the model creation process. The image dataset used had a size of 300x300 pixels. In the classification testing of the SIBI alphabet results in this study, it was tested using distance tests. The distance between the webcam and the SIBI language speaker was divided into two categories: 40 cm and 60 cm. For a 40-cm distance, an accuracy of 87.50% was obtained, and for a 60-cm distance, an accuracy of 79.17% was achieved. One limitation of this study is that two alphabets, J and Z, were not included in the dataset. This is because recognition of these two alphabets requires not only finger pattern recognition but also recognition of their gesture patterns.

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“…Thus, a total balanced database of 22,400 consisting of 700 samples (images) was generated for each class. Thus, for recognition and translation, 500 images from each of the 32 classes (16,000) are used for training, 100 images from each of the 32 classes (3,200) for validation and 100 images from each of the 32 classes (3,200) for testing.…”

Section: A Data Collectionmentioning

confidence: 99%

“…Sign language is the form of communication for people with hearing or speech problems, where movements or expressions of the human body are used to interact with the environment and be understood [1]- [3]. Sign languages are specific to each country, in some cases they are similar, but they always vary between them.…”

Section: Introductionmentioning

confidence: 99%

Peruvian Sign Language translator for people with hearing and/or communication disabilities using a convolutional neural network

Briones-Cerquín,

Tumay Guevara,

Paiva Peredo

et al. 2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

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Promoting the social inclusion of people with deafness and/or communication disabilities is a priority for many countries. For this reason, considerable emphasis is currently being given to machine learning (ML) and deep learning (DL) techniques for sign language recognition and translation, as they can be a significant contribution to the social inclusion of the deaf and/or deafened community. Thus, the present research proposes the development of a translator of Peruvian Sign Language (PSL) for the recognition and translation of static signs belonging to PSL through a convolutional neural network (CNN). These signs are the numbers from 0 to 9 and the letters of the alphabet except J, Ñ and Z because they are represented with movable signs and the letters O and W because they are very similar to the numbers "0" and "6". To achieve the development of the translator, a balanced database for PSL was built from scratch, consisting of 700 images for each static sign, for a total of 22400 images. These images have a dimension of 80x80 pixels that go through a preprocessing stage, 3 convolutional layers, filters, kernels, ReLu and MaxPooling activation functions. Experimental results show that the translator recognizes static PSL signs with an accuracy of 90%, 86% and 81% for training, validation and testing respectively.

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Indonesia Sign Language Recognition using Convolutional Neural Network

Cited by 5 publications

References 20 publications

A Deep Learning-Based Approach for Hand Sign Recognition Using CNN Architecture

A Deep Learning-Based Approach for Hand Sign Recognition Using CNN Architecture

SIBI (Sistem Bahasa Isyarat Indonesia) berbasis Machine Learning dan Computer Vision untuk Membantu Komunikasi Tuna Rungu dan Tuna Wicara

Peruvian Sign Language translator for people with hearing and/or communication disabilities using a convolutional neural network

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