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You searched for subject:(Indian Sign Language). Showing records 1 – 2 of 2 total matches.

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1. Daleesha M Viswanathan; Dr. Sumam Mary Idicula. A Computational Framework for Indian Sign Language Recognition.

Degree: 2015, Cochin University of Science and Technology

Sign language is the primary means of communication for the hard to hear and speak people around the globe. Sign language emphasizes on visual possibilities as the participants are unable to hear sound patterns. Sign language uses different signs, body postures and gestures as opposed to sound patterns for communication, and evolves like any other spoken language. American Sign Language (ASL), British sign language (BSL), Arabic sign language (ArSL), Chinese sign language (CSL) and Indian sign language (ISL) are some of the widely used sign language systems around the world. There exists significant variation between sign languages, and due to these inherent variations, it is not possible to fully adopt a methodology that is found suitable for all. There are enormous complexities in ISL. Contrary to ASL, ISL sentences follow Subject-Object-Verb pattern. For example, the relative positioning of hand on face with respect to nose can convey ‘WOMAN’ or ‘THINK’ in ISL. Such complexities necessitate independent research in ISL. Sign language recognition involves integration of different categories of signs. The signs can be mainly categorized into three groups like static hand gestures, dynamic gestures and facial expression. This research focuses on these three different channels and work to identify the potential of different computational methods to address some of the associated complexities with each channel. These complexities include static gestures with resemblances, static overlaid gestures, differential movement and directional changes in dynamic gestures and facial expression changes.

Subjects/Keywords: Indian Sign Language; Sign Language Recognition; Indian Sign Language; American Sign Language Recognition; Arabic Sign Language Recognition; Chinese Sign Language Recognition

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APA (6th Edition):

Idicula, D. M. V. D. S. M. (2015). A Computational Framework for Indian Sign Language Recognition. (Thesis). Cochin University of Science and Technology. Retrieved from http://dyuthi.cusat.ac.in/purl/5144

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Idicula, Daleesha M Viswanathan; Dr. Sumam Mary. “A Computational Framework for Indian Sign Language Recognition.” 2015. Thesis, Cochin University of Science and Technology. Accessed January 25, 2020. http://dyuthi.cusat.ac.in/purl/5144.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Idicula, Daleesha M Viswanathan; Dr. Sumam Mary. “A Computational Framework for Indian Sign Language Recognition.” 2015. Web. 25 Jan 2020.

Vancouver:

Idicula DMVDSM. A Computational Framework for Indian Sign Language Recognition. [Internet] [Thesis]. Cochin University of Science and Technology; 2015. [cited 2020 Jan 25]. Available from: http://dyuthi.cusat.ac.in/purl/5144.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Idicula DMVDSM. A Computational Framework for Indian Sign Language Recognition. [Thesis]. Cochin University of Science and Technology; 2015. Available from: http://dyuthi.cusat.ac.in/purl/5144

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

2. Krishnaveni M. View-based signer independent approach for indian sign language recognition using extreme learning machine.

Degree: Computer Science, 2012, Avinashilingam Deemed University For Women

Sign language is a collection of different kinds of sign pattern to communicate the thoughts of a signer. It is commonly used by the hearing impaired people who neither speak nor hear. As Indian nation has the historical newlinebackground of language movement, it reminds that everyone has the right to communicate using their own language. Similarly, sign language has also been promoted with oral language as an intermediate of interaction and exchanging of ideas. Two approaches are commonly used to recognize gestures in human newlinecomputer interface. One is glove-based and the other is vision-based. The glove-based approach uses gloves and sensors as its measuring device for analyzing the hand movements. This system suffers from the limitation of using a device which is intrusive both for signer and the audience. Vision-based gesture recognition is based on the appearance of the user hand and uses newlinetemplate images or features for its recognition purpose. This approach is newlinetherefore best suited as it is user-friendly and always been a recommended model for gesture analysis. The objective of the proposed research work is to formulate a new viewbased technology for recognition and translation of Indian Sign Language in newlineorder to facilitate the deaf community towards new e-Services. The Indian Sign newlineLanguage has two-hand dominant signs and one-hand dominant signs for letters and numbers. The main focus of the work is on one-hand dominant and to device a recognition approach that can recognize the numeric, vowel, consonant signs using image processing and computational intelligence.

References and List of publications included

Advisors/Committee Members: Radha V.

Subjects/Keywords: Computer Science; View-based signer; Indian sign language recognition; Extreme Learning Machine

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

M, K. (2012). View-based signer independent approach for indian sign language recognition using extreme learning machine. (Thesis). Avinashilingam Deemed University For Women. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/13306

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

M, Krishnaveni. “View-based signer independent approach for indian sign language recognition using extreme learning machine.” 2012. Thesis, Avinashilingam Deemed University For Women. Accessed January 25, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/13306.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

M, Krishnaveni. “View-based signer independent approach for indian sign language recognition using extreme learning machine.” 2012. Web. 25 Jan 2020.

Vancouver:

M K. View-based signer independent approach for indian sign language recognition using extreme learning machine. [Internet] [Thesis]. Avinashilingam Deemed University For Women; 2012. [cited 2020 Jan 25]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/13306.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

M K. View-based signer independent approach for indian sign language recognition using extreme learning machine. [Thesis]. Avinashilingam Deemed University For Women; 2012. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/13306

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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