Indian Sign Language Recognition Using Eigen Value Weighted Euclidean Distance Based Classification Technique

Singha, Joyeeta; Das, Kishore Kumar

doi:10.14569/ijacsa.2013.040228

Cited by 86 publications

(40 citation statements)

References 19 publications

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“…[12] Proposed a system for recognizing ISL gestures using Eigen value weighted Euclidean distance as a classification technique. Gabor filters are convolved with images to acquire the features and the feature space is reduced…”

Section: Related Workmentioning

confidence: 99%

Static Hand Gesture Recognition for Sign Language Alphabets using Edge Oriented Histogram and Multi Class SVM

Nagarajan¹,

Subashini²

2013

IJCA

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In recent years, enormous research is progressing in the field of Computer Vision and Human Computer Interaction where hand gestures play a vital role. Hand gestures are more powerful means of communication for hearing impaired when they communicate to the normal people everywhere in day to day life. As the normal people find little difficulty in recognizing and interpreting the meaning of sign language expressed by the hearing impaired, it is inevitable to have an interpreter for translation of sign language. To overcome this difficulty, an automatic hand gesture recognition system which translates the sign language into text needs to be developed. In this paper, a static hand gesture recognition system for American Sign Language using Edge Oriented Histogram (EOH) features and multiclass SVM is proposed. The edge histogram count of input sign language alphabets is extracted as the features and applied to a multiclass SVM for classification. The average accuracy of the system is compared with different number of features and the experimental findings demonstrate that the proposed method gives a success rate of 93.75%.

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Section: Related Workmentioning

confidence: 99%

Static Hand Gesture Recognition for Sign Language Alphabets using Edge Oriented Histogram and Multi Class SVM

Nagarajan¹,

Subashini²

2013

IJCA

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“…[10] We have proposed a system, which can see the different alphabets of Indian Sign Language for Human-Computer participation giving more exact results in any occasion possible time. [11] II. LITERATURE REVIEW Sirshendu Hore et al [12] 2017, had proposed an exact sign language recognition framework can energize more correct correspondence of deaf and dumb individuals.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of Sign Language Communication Gadget via Structural Redesigning of Artificial Intelligence Technique

Walde¹

2018

IJRASET

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“…Among them American Sign Language (ASL) and British Sign Language (BSL) are based on English language [3], [4] [5] whereas, German Sign Language (GSL) [6], Argentinian Sign Language (ArSL) [7], Indian Sign Language (ISL) [8], Persian sign language [9], Arabian Sign Language [10] and Chinese Sign Language (CSL) [11] are also among the wellknown sign languages. However very little work has been done on PSL (Pakistan Sign Language) [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Repository of Static and Dynamic Signs

Saqib¹,

Kazmi²

2017

ijacsa

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Abstract-Gesture-based communication is on the rise in Human Computer Interaction. Advancement in the form of smart phones has made it possible to introduce a new kind of communication. Gesture-based interfaces are increasingly getting popular to communicate at public places. These interfaces are another effective communication medium for deaf and dumb. Gestures help conveying inner thoughts of these physically disabled people to others thus eliminating the need of a gesture translator. These gestures are stored in data sets so we need to work on developing an efficient dataset. Many datasets have been developed for languages like American sign language; however, for other sign languages, like Pakistani Sign Language (PSL), there has not been done much work. This paper presents technique for storing datasets for static and dynamic signs for any sign language other than British Sign Language or American Sign Language. For American and British Sign Languages many datasets are available publicly. However, other regional languages lack public availability of data sets. Pakistan Sign Language has been taken as a case study more than 5000 gestures have been collected and they will be made part of a public database as a part of this research. The research is first initiative towards building Universal Sign Language as every region has a different Sign Language. The second focus of the research is to explore methodologies where a signer communicates without any constraint like Data gloves or a particular background. Thirdly, the paper proposes use of spelling based gestures for easier communication. So the dataset design suggested is not affected by constraints of any kind.

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Indian Sign Language Recognition Using Eigen Value Weighted Euclidean Distance Based Classification Technique

Cited by 86 publications

References 19 publications

Static Hand Gesture Recognition for Sign Language Alphabets using Edge Oriented Histogram and Multi Class SVM

Static Hand Gesture Recognition for Sign Language Alphabets using Edge Oriented Histogram and Multi Class SVM

Design and Development of Sign Language Communication Gadget via Structural Redesigning of Artificial Intelligence Technique

Repository of Static and Dynamic Signs

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