An intelligent framework for recognizing sign language from continuous video sequence using boosted subunits

Elakkiya, R.; Selvamani, K.; Kannan, A.

doi:10.1049/ic.2013.0329

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Real-Boost [10], LP-Boost [5]. We focus on the discrete AdaBoost (adaptive boosting) algorithm which is already presented in [22]. The probability p(x) is updated such that it increases for the samples that are misclassified.…”

Section: Boosting Algorithmmentioning

confidence: 99%

An Active Learning Framework for Human Hand Sign Gestures and Handling Movement Epenthesis Using Enhanced Level Building Approach

Elakkiya

Selvamani

2015

Procedia Computer Science

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Human hand detection and segmentation plays an important role in sign language recognition and human machine interaction. In this paper, a novel approach for learning a vision-based hand detection system is introduced. The main contribution of this paper includes robust boosting-based framework for real-time detection of a hand in unconstrained and heterogeneous environments. The proposed system makes use of efficient representative features which allows fast computation while changing the hand appearances and background. Moreover, this proposed strategy efficiently improves the performance while reducing the effort of hand labeling. Experimental results show that the proposed method is practically more flattering as it meets the requirements of real-time performance, accuracy and robustness. This system has been proved to work well with a reasonable amount of training samples and was computationally found to be more effective and efficient.

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Section: Boosting Algorithmmentioning

confidence: 99%

An Active Learning Framework for Human Hand Sign Gestures and Handling Movement Epenthesis Using Enhanced Level Building Approach

Elakkiya

Selvamani

2015

Procedia Computer Science

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“…In addition, due to the high cost of sensors, practical applications of sensor-based SLR systems are impractical. On the other hand, vision-based SLR systems have profoundly affected researchers due to their heaviness and capacity to manage crowded, dynamic heterogeneous surroundings and fluctuations in the segmentation stage under varying illuminations and occlusions ( Elakkiya, Kannan & Selvamani, 2013 ). The SLR solutions’ standard element automatically allows signer-dependent actions, i.e., all signers are trained before involving the patient.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Urdu sign language: a systematic review of the machine learning classification

Zahid

Rashid

Hussain³

et al. 2022

PeerJ Computer Science

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Background and Objective Humans communicate with one another using language systems such as written words or body language (movements), hand motions, head gestures, facial expressions, lip motion, and many more. Comprehending sign language is just as crucial as learning a natural language. Sign language is the primary mode of communication for those who have a deaf or mute impairment or are disabled. Without a translator, people with auditory difficulties have difficulty speaking with other individuals. Studies in automatic recognition of sign language identification utilizing machine learning techniques have recently shown exceptional success and made significant progress. The primary objective of this research is to conduct a literature review on all the work completed on the recognition of Urdu Sign Language through machine learning classifiers to date. Materials and methods All the studies have been extracted from databases, i.e., PubMed, IEEE, Science Direct, and Google Scholar, using a structured set of keywords. Each study has gone through proper screening criteria, i.e., exclusion and inclusion criteria. PRISMA guidelines have been followed and implemented adequately throughout this literature review. Results This literature review comprised 20 research articles that fulfilled the eligibility requirements. Only those articles were chosen for additional full-text screening that follows eligibility requirements for peer-reviewed and research articles and studies issued in credible journals and conference proceedings until July 2021. After other screenings, only studies based on Urdu Sign language were included. The results of this screening are divided into two parts; (1) a summary of all the datasets available on Urdu Sign Language. (2) a summary of all the machine learning techniques for recognizing Urdu Sign Language. Conclusion Our research found that there is only one publicly-available USL sign-based dataset with pictures versus many character-, number-, or sentence-based publicly available datasets. It was also concluded that besides SVM and Neural Network, no unique classifier is used more than once. Additionally, no researcher opted for an unsupervised machine learning classifier for detection. To the best of our knowledge, this is the first literature review conducted on machine learning approaches applied to Urdu sign language.

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“…While in the image-based technique, a sequence of images was captured by using a camera. Then, those images were processed by using an image-processing method [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Indonesian sign language recognition using kinect and dynamic time warping

Khotimah

Suciati

Anggita

2019

IJEECS

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Sign Language Recognition System (SLRS) is a system to recognise sign language and then translate them into text. This system can be developed by using a sensor-based technique. Some studies have implemented various feature extraction and classification methods to recognise sign language in the different country. However, their systems were user dependent (the accuracy was high when the trained and the tested user were the same people, but it was getting worse when the tested user was different to the trained user). Therefore in this study, we proposed a feature extraction method which is invariant to a user. We used the distance between two users’ skeleton instead of using the users’ skeleton positions because the skeleton distance is independent to the user posture. Finally, forty-five features were extracted in this proposed method. Further, we classified the features by using a classification method that is suitable with sign language gestures characteristic (time-dependent sequence data). The classification method is Dynamic Time Wrapping. For the experiment, we used twenty Indonesian sign languages from different semantic groups (greetings, questions, pronouns, places, family and others) and different gesture characteristic (static gesture and dynamic gesture). Then the system was tested by a different user with the user who did the training. The result was promising, this proposed method produced high accuracy, reach 91% which shows that this proposed method is user independent.

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An intelligent framework for recognizing sign language from continuous video sequence using boosted subunits

Cited by 6 publications

References 9 publications

An Active Learning Framework for Human Hand Sign Gestures and Handling Movement Epenthesis Using Enhanced Level Building Approach

An Active Learning Framework for Human Hand Sign Gestures and Handling Movement Epenthesis Using Enhanced Level Building Approach

Recognition of Urdu sign language: a systematic review of the machine learning classification

Indonesian sign language recognition using kinect and dynamic time warping

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