Real time Sign Language Recognition using PCA

Sawant, Shreyashi Narayan; Kumbhar, Mahesh

doi:10.1109/icaccct.2014.7019333

Cited by 39 publications

(6 citation statements)

References 6 publications

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“…In the study of Sawant and Kumbhar [13], the authors used Euclidean distance to calculate the difference between testing and training images, and then gestures can be recognized.…”

Section: Iiii Recognition Methodsmentioning

confidence: 99%

A Saudi Sign Language Recognition System based on Convolutional Neural Networks

Al-Obodi¹,

Al-Hanine²,

Al-Harbi³

et al. 2020

International Journal of Engineering Research and Technology

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Sign language is the main communication method for deaf people. It is a collection of signs that deaf people use to deal with each other. Deaf people find it difficult to communicate with normal people, as most of them do not understand the signs of the sign language. Sign language recognition systems translate the signs into natural languages and thus shorten the gap between deaf and normal people. Many studies have been done on different sign languages. There is a considerable number of studies on the standard Arabic sign language. In Saudi Arabia, deaf people use the Saudi language, which is different from standard Arabic. This study proposes a smart recognition system for Saudi sign language based on convolutional neural networks. The system is based on the Saudi Sign language dictionary, which was published recently in 2018. In this study, we constructed a dataset of 40 Saudi signs with about 700 images for each sign. We then developed a deep convolutional neural network and trained it on the constructed dataset. To have better recognition, we took images of the signs with different hand sizes, skin colors, lighting, backgrounds, and with/without accessories. The results showed that the recognition model achieved an accuracy of 97.69% for training data and 99.47% for testing data. The model was implemented in two versions: mobile and desktop.

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“…In the study of Sawant and Kumbhar [13], the authors used Euclidean distance to calculate the difference between testing and training images, and then gestures can be recognized.…”

Section: Iiii Recognition Methodsmentioning

confidence: 99%

A Saudi Sign Language Recognition System based on Convolutional Neural Networks

Al-Obodi¹,

Al-Hanine²,

Al-Harbi³

et al. 2020

International Journal of Engineering Research and Technology

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“…In the research [11] the combination of PCA with MFCC had increased the recognition rates obtained with noisy speech signals from 63.9% to 75.0%. However, less accuracy is obtained using PCA for spontaneous and continuous speech recognition [12].…”

Section: Speech Features Extraction Techniquesmentioning

confidence: 99%

Automatic Speech Recognition Features Extraction Techniques: A Multi-criteria Comparison

Labied¹,

Belangour²

2021

IJACSA

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Features extraction is an important step in Automatic Speech Recognition, which consists of determining the audio signal components that are useful for identifying linguistic content while removing background noise and irrelevant information. The main objective of features extraction is to identify the discriminative and robust features in the acoustic data. The derived feature vector should possess the characteristics of low dimensionality, long-time stability, nonsensitivity to noise, and no correlation with other features, which makes the application of a robust feature extraction technique a significant challenge for Automatic Speech Recognition. Many comparative studies have been carried out to compare different speech recognition feature extraction techniques, but none of them have evaluated the criteria to be considered when applying a feature extraction technique. The objective of this work is to answer some of the questions that may arise when considering which feature extraction techniques to apply, through a multicriteria comparison of different features extraction techniques using the Weighted Scoring Method.

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“…By disposing the components with low information and taking into account the remaining ones as new variables, it allows for dimensionality reduction without loosing information. As a technique it has been widely used in the gestural as well as the sign language domain either as a visualization technique or as a pre-processing stage prior to other machine and deep learning stages (Gweth et al, 2012;Sawant and Kumbhar, 2014;Haque et al, 2019;Gao et al, 2021).…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Sign and Search: Sign Search Functionality for Sign Language Lexica

Fragkiadakis¹,

Putten²

2021

Preprint

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Sign language lexica are a useful resource for researchers and people learning sign languages. Current implementations allow a user to search a sign either by its gloss or by selecting its primary features such as handshape and location. This study focuses on exploring a reverse search functionality where a user can sign a query sign in front of a webcam and retrieve a set of matching signs. By extracting different body joints combinations (upper body, dominant hand's arm and wrist) using the pose estimation framework OpenPose, we compare four techniques (PCA, UMAP, DTW and Euclidean distance) as distance metrics between 20 query signs, each performed by eight participants on a 1200 sign lexicon. The results show that UMAP and DTW can predict a matching sign with an 80% and 71% accuracy respectively at the top-20 retrieved signs using the movement of the dominant hand arm. Using DTW and adding more sign instances from other participants in the lexicon, the accuracy can be raised to 90% at the top-10 ranking. Our results suggest that our methodology can be used with no training in any sign language lexicon regardless of its size.

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Real time Sign Language Recognition using PCA

Cited by 39 publications

References 6 publications

A Saudi Sign Language Recognition System based on Convolutional Neural Networks

A Saudi Sign Language Recognition System based on Convolutional Neural Networks

Automatic Speech Recognition Features Extraction Techniques: A Multi-criteria Comparison

Sign and Search: Sign Search Functionality for Sign Language Lexica

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