Hand Gesture Recognition for Sign Language: A New Higher Order Fuzzy HMM Approach

Darwish, Saad M.; Madbouly, Magda M.; Khorsheed, Murad B.

doi:10.7763/ijet.2016.v8.877

Cited by 2 publications

(2 citation statements)

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“…The system's accuracy achieved a satisfactory level of 84.2% when evaluated with signs comprising 180 digits and 240 alphabets. Darwish et al (2016) have presented several results concerning static hand gesture recognition using an algorithm based on Type-2 Fuzzy HMM (T2FHMM). The features used as observables in the training as well as in the recognition phases are based on Singular Value Decomposition (SVD) that optimally exposes the geometric structure of a matrix.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition of static gestures using correlation and cross-correlation

Saqib¹,

Kazmi²

2018

Int. j. adv. appl. sci.

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Sign language recognition has been an active area of research for around two decades and numerous sign languages have been extensively studied in order to design reliable sign language recognition systems. Pakistan sign language (PSL) has been used as a case study here. A comprehensive database of static images depicting the signs for different Urdu alphabets is being used as a reference and input images are being compared to perform PSL alphabet recognition. The normalized Correlation technique is being used for image registration between input image and images from the database to find the closest match. The purpose of research is to identify static gestures of any Sign Language which can ultimately lead to an identification of words and sentences. The research starts with image acquisition, image preprocessing, and use of correlation and labeling of an identified symbol. Normalized correlation is used to find the nearest match. This paper includes experiments for 37 static hand gestures related to PSL alphabets. Training dataset consists of 10 samples of each PSL symbol in different lighting conditions, different sizes and shapes of hand by 5 different signers. This gesture recognition system can identify one hand static gestures in any complex background with a "minimum-possible constraints" approach. A comparison is also drawn between normalized correlations and normalized cross-correlation. As compared to other technique, this technique can work with a small dataset size. The technique is based on unsupervised learning.

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Section: Literature Reviewmentioning

confidence: 99%

Recognition of static gestures using correlation and cross-correlation

Saqib¹,

Kazmi²

2018

Int. j. adv. appl. sci.

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“…(T2FHMM). In addition to improved classification than the classical Hidden Markov model, T2FHMM addresses noise and morphological uncertainties in hand signs [3]. Asif used two tests to sign methods to detect the Urdu sign method.…”

Section: Introductionmentioning

confidence: 99%

Urdu Sign Language Reorganization via Artificial Neural Networks

Yasir¹,

Li²,

Malik³

2021

Materials, Computer Engineering and Education Technology

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Sign languages display the same linguistic characteristics as oral languages and utilize the same language services. Sign language processing solutions provide a communication link for persons with hearing impairments and healthy persons. Without these icons' ability to understand, deaf children experience several challenges in learning social norms and cannot meet adults to exchange knowledge. Parents find it challenging to express their messages to their deaf children and not hear their children. This paper focused on establishing Urdu sign language to reduce the communication barrier between ordinary folks and physically impaired people. The present study observed the Urdu Sign Language in deaf children. In this paper, the process of detecting Urdu sign language alphabets is proposed. All the 37 alphabets are identified by using KNN, ANN, and SVM classifiers. Through these alphabets, the teachers at schools and the parents at home can communicate efficiently with their deaf children. Histogram of Gradient technique is used for feature extraction. Urdu Alphabetic are identified. Maximum accuracy is obtained by using a KNN classifier that was 99, which is a significant contribution. Our proposed results are comparable to the state of the art techniques.

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Hand Gesture Recognition for Sign Language: A New Higher Order Fuzzy HMM Approach

Cited by 2 publications

References 5 publications

Recognition of static gestures using correlation and cross-correlation

Recognition of static gestures using correlation and cross-correlation

Urdu Sign Language Reorganization via Artificial Neural Networks

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