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Review Paper: Detail Study for Sign Language Recognization Techniques

Ramesh M. Kagalkar, Dr. S.V. Gumaste

Abstract


This paper reviews the intensive state of the art in automatic recognition of continuous signs, from  different languages, supported the information  sets used, features computed, technique used, and recognition rates achieved. In this paper discover that, in the past, most work has been tired finger-spelled words and isolated sign recognition, but recently, there has been vital progress within the recognition of signs embedded briefly continuous sentences. Paper tend to conjointly realize that researchers are getting down addressing the necessary downside of extracting and integration non-manual data that is gift in face and head movement and present results from experiments integration of non-manual options.


Keywords


American Sign Language (ASL), Hidden Marko Model (HMM) and Extended Multi Modal Annotation (EMMA).

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References


Cox, S., Lincoln, M., Tryggvason, J., Nakisa, M., Wells, M., Tutt, M., Abbott, S.: TESSA,a system to aid communication with deaf people. In: Proceedings of the fifth internationalACM conference on Assistive technologies, ACM Press (2002) 205–212

Phelps, K.: Signing avatar characters become virtual tutors. In: Virtual Voice. (2002)3. Toro, J., et al.: An improved graphical environment for transcription and display of AmericanSign Language. Information 4 (2001) 533–539

Akyol, S., Canzler, U.: An information terminal using vision based sign language recognition.In: ITEAWorkshop on Virtual Home Environments. (2002) 61–68

Sagawa, H., Takeuchi, M.: Development of an information kiosk with a sign language recognitionsystem. In: Conference on Universal Usability. (149–150) 2000

Kramer, J., Leifer, L.: The talking glove: An expressive and receptive ’verbal’ communicationaid for the deaf, deaf-blind and nonvocal. In: Conference on Computer Technology,Special Education, and Rehabilitation. (1987)

Murakami, K., Taguchi, H.: Gesture ecognition using recurrent neural networks. In:SIGCHI Conference Proceedings. (237–242) 1991

Charayaphan, C., Marble, A.: Image processing system for interpreting motion in AmericanSign Language. Journal of Biomedical Engineering 14 (1992) 419–425

Waldron, M., Kim, S.: Isolated ASL recognition system for deaf persons. IEEE Transactionson Rehabilitation Engineering 3 (1995) 261

Kadous, M.W.: Machine translation of AUSLAN signs using powergloves: Towards largelexicon-recognition of sign language. In: Workshop on the integration of Gesture in Languageand Speech. (1996) 165–174

Vamplew, P.: Recognition of Sign Language Using Neural Networks. PhD thesis, Departmentof Computer Science, University of Tasmania (1996)

Lee, C., Xu, Y.: Online, interactive learning of gestures for human robot interfaces. In: IEEEInternational Conference on Robotics and Automation. (1996) 2982–2987

Al-Jarrah, O., Halawani, A.: Recognition of gestures in Arabic sign language using neurofuzzysystems. Artificial Intelligence 133 (2001) 117–138

Fang, G., Gao, W., Zhao, D.: Large sign vocabulary sign recognition based on hierarchicaldecision tree. In: International Conference on Multimodal Interfaces. (2003) 125–131

Messing, L., Erenshteyn, R., Foulds, R., Galuska, S., Stern, G.: American Sign Languagecomputer recognition: Its present and its promise. In: ISAAC Conference. (1994)

Hernandez-Rebollar, J.L., Lindeman, R.W., Kyriakopoulos, N.: A multi-class pattern recognitionsystem for practical finger spelling translation. In: The 4th IEEE International Conferenceon Multimodal Interfaces. (2002) 185

Vassilia, P.N., Konstantinos, M.G.: Towards an assistive tool for Greek Sign Languagecommunication. In: IEEE International Conference on Advanced Learning Technologies(ICALT’03). (125) 2003

Starner, T., Pentland, A.: Computer-based visual recognition of American Sign Language.In: International Conference on Theoretical Issues in Sign Language Research. (1996)

Braffort, A.: ARGo: An architecture for sign language recognition and interpretation. In:Progress in Gestural Interaction. (1996) 17–30

Grobel, K., Assan, M.: Isolated sign language recognition using Hidden Markov Models. In:International Conference System: Man and Cybernetics. (1996) 162–167

Liang, R., M.Ouhyoung.: A real-time continuous gesture recognition system for sign language.In: International Conference on Automatic Face and Gesture Recognition. (1998)558–565

Vogler, C., Metaxas, D.: Handshapes and movements: Multiple-channel ASL recognition.In: Lecture Notes in Artificial Intelligence 2915. (2004) 247–258

Ma, J., Gao,W.,Wang, C.,Wu, J.: A continuous Chinese Sign Language recognition system.In: International Conference on Automatic Face and Gesture Recognition. (2000) 428–433

Chen, Y.: Chinese Sign Language recognition and synthesis. In: IEEE International Workshopon Analysis and Modeling of Faces and Gestures. (2003)

Kapuscinski, T.,Wysocki, M.: Vision-based recognition of Polish Sign Language. In: Methodsin Artificial Intelligence. (2003)

Starner, T.: Visual recognition of American Sign Language using Hidden Markov Models.Master’s thesis, MIT, Media Lab. (1995)

Vogler, C., Metaxas, D.: Adapting Hidden Markov Models for ASL recognition by usingthree-dimensional computer vision methods. In: International Conference Systems on Manand Cybernetics. (1997) 156–161.

Starner, T., Weaver, J., Pentland, A.: A wearable computer based American Sign Languagerecognizer. In: International Symposium on Wearable Computers. (1997) 130–137.

Assan, M., Grobel, K.: Video-based sign language recognition using Hidden Markov Models.In: International Gesture Workshop: Gesture and Sign Language in Human-ComputerInteraction. (1998) 97–109.

Vogler, C., Metaxas, D.: ASL recognition based on a coupling between HMMs and 3Dmotion analysis. In: International Conference on Computer Vision. (363–369) 1998.

Hienz, K., Bauer, B., Kraiss, K.: HMM-based continuous sign language recognition usingstochastic grammars. In: Gesture Workshop. (1999).

Bauer, B., Hienz, H., Kraiss, K.F.: Video-based continuous sign language recognition usingstatistical methods. In: International Conference on Pattern Recognition. (2000) 463–466.

Vogler, C., Sun, H., Metaxas, D.: A framework for motion recognition with application toAmerican Sign Language and gait recognition. In: Workshop on Human Motion. (2000)33–38.

Bauer, B., Kraiss, K.F.: Towards an automatic sign language recognition system using subunits.In: International Gesture Workshop: Gesture and Sign Language in Human-ComputerInteraction. (2002) 64–7535. Brashear, H., Starner, T., Lukowicz, P., Junker, H.: Using multiple sensors for mobile signlanguage recognition. In: IEEE International Symposium on Wearable Computers. (2003).

Starner, T., Pentland, A.P.: Real-time American Sign Language recognition from video usingHidden Markov Models. In: Symposium on Computer Vision. (1995) 265–270

Vogler, C., Metaxas, D.: Toward scalability in ASL recognition: Breaking down signs intophonemes. In: Gesture-Based Communication in Human-Computer Interaction. (211-224)1999

Vogler, C., Metaxas, D.: Parallel Hidden Markov Models for American Sign Languagerecognition. In: International Conference on Computer Vision. (116–122) 1999.

Parashar, A.: Representation and interpretation of manual and non-manual informationfor automated American Sign Language Recognition. Master’s thesis, University of SouthFlorida (2003)

Bauer, B., Hienz, H.: Relevant features for video-based continuous sign language recognition.In: International Conference on Automatic Face and Gesture Recognition. (2000)440–445

Vogler, C., Metaxas, D.: A framework of recognizing the simultaneous aspects of AmericanSign Language. Computer Vision and Image Understanding 81 (2001) 358–384.

Ramesh Kagalkar and Dr. Nagaraja H.N “New Methodology for Translation of Static Sign Symbol to Words in Kannada Language”,” International Journal of Computer Applications Volume 121, Page No 26-30,July-2015.

Ramesh M. Kagalkar , Dr. Nagaraja H.N , Dr. S.V Gumaste, ”A Novel Technical Approach for Implementing Static Hand Gesture Recognition”, International Journal of Advanced Research in Computer and Communication Engineering ISSN (Online) 2278-1021 ISSN (Print) 2319-5940 Vol. 4, Issue 7, July 2015.

Amit kumar and Ramesh Kagalkar “Sign Language Recognition for Deaf User”, Internal Journal for Research in Applied Science and Engineering Technology, Volume 2 Issue XII, December 2014.

Amit kumar and Ramesh Kagalkar “ Advanced Marathi Sign Language Recognition using Computer Vision” , International Journal of Computer Applications (0975 – 8887) Volume 118 – No. 13, May 2015.

Amit kumar and Ramesh Kagalkar “Methodology for Translation of Sign Language into Textual Version in Marathi”, CiiT, International Journal of Digital Image Processing, Volume 07, No.08 , Aug 2015.

Amit kumar and Ramesh Kagalkar,“ Sign Language to Text and Vice Versa Recognition using Computer Vision in Marathi”, International Journal of Computer Applications (0975 – 8887) National Conference on Advances in Computing (NCAC 2015)

Rashmi B. Hiremath and Ramesh Kagalkar,“ Review Paper on Sign Language Recognition Techniques”, International Journal of Computer Applications (0975 – 8887) National Conference on Advances in Computing (NCAC 2015)

Neidle, C., MacLaughlin, D., Bahan, B., G., L.R., Kegl, J.: The SignStream project. In:American Sign Language Linguistic Research Project, Report 5 Boston University. (1997)

Larson, J.A.: EMMA: W3C’s extended multimodal annotation markup language. SpeechTechnology Magazine 8 (2003) http://www.w3.org/TR/emma/

Vandana D. Edke and Ramesh Kagalkar,“ Review Paper on Video Content Analysis into Text Description”, International Journal of Computer Applications (0975 – 8887) National Conference on Advances in Computing (NCAC 2015)

Vasundhara Kadam and Ramesh Kagalkar,“ Review on Textual Description of Image Contents”, International Journal of Computer Applications (0975 – 8887) National Conference on Advances in Computing (NCAC 2015)

Mrunmayee and Ramesh Kagalkar “A Review On Conversion Of Image To Text As Well As Speech Using Edge Detection And Image Segmentation” International Journal of Advance Research in Computer Science Management Studies, Volume 2, Issue 11 (November-2014) publish on 29th November to 30th November 2014.

Mrunmayee Patil and Ramesh Kagalkar “ An Automatic Approach for Translating Simple Images into Text Descriptions and Speech for Visually Impaired People” , International Journal of Computer Applications (0975 – 8887) Volume 118 – No. 3, May 2015.

Mrunmayee Patil and Ramesh Kagalkar,“ A New Approach for Generating Text Description of Images and Speech Synthesis”, International Journal of Computer Applications (0975 – 8887) National Conference on Advances in Computing (NCAC 2015)

Kaveri Kamble and Ramesh Kagalkar, “A Review: Translation of Text to Speech Conversion for Hindi Language” , International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064, Vol. 3 Issue 11, November 2014.

Kaveri Kamble and Ramesh Kagalkar, “Audio Visual Speech Synthesis and Speech Recognition for Hindi Language” , International Journal of Computer Science and Information Technologies(IJCSIT) ISSN (Online): 0975-9646, Vol. 6 Issue 2, April 2015.

Kaveri Kamble and Ramesh Kagalkar “ A Novel Approach for Hindi Text Description to Speech and Expressive Speech Synthesis” International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868 Foundation of Computer Science FCS, New York, USA Volume 8– No.7, May 2015 – www.ijais.org.

Kaveri Kamble and Ramesh Kagalkar,“ A New Approach of Emotion and Facial Expression Detection of Speaker with Conversion of Text To Speech and Vice Versa for Hindi Language”, International Journal of Computer Applications (0975 – 8887) National Conference on Advances in Computing (NCAC 2015)

Shivaji Chaudhari and Ramesh Kagalkar “A Review of Automatic Speaker recognization and Identifying Speaker Emotion Using Voice Signal” International Journal of Science and Research (IJSR), Volume 3, Issue 11 November 2014.

Shivaji Chaudhari and Ramesh Kagalkar “ Automatic Speaker Age Estimation and Gender Dependent Emotion Recognition” , International Journal of Computer Applications (0975 – 8887) Volume 117 – No. 17, May 2015.

Shivaji J. Chaudhari and Ramesh M Kagalkar, “A Methodology for Efficient Gender Dependent Speaker Age and Emotion Identification System”, International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE) ISSN 2319-5940, Volume 4, Issue 7, July 2015.

Shivaji Chaudhari and Ramesh Kagalkar,“ Methodology for Gender Identification,Classification and Recognition of Human Age”, International Journal of Computer Applications (0975 – 8887) National Conference on Advances in Computing (NCAC 2015)

Ajay R. Kadam and Ramesh Kagalkar,“ Audio Scenarios Detection Technique”, International Journal of Computer Applications (IJCA),Volume 120 June 2015 Edition ISBN : 973-93-80887-55-4.

Ajay R. Kadam and Ramesh Kagalkar,“ A Review Paper on Predictive Sound Recognition System”, CiiT International Journal of Software Engineering andTechnology, June Issue 2015 on 30/6/2015.

jay R. Kadam and Ramesh Kagalkar,“ Predictive Sound Recognition System ”, International Journal of Advance Research in Computer Science and Management Studies, Volume 2, Issue11 ISSN(Online):2321-7782.

Ajay R. Kadam and Ramesh Kagalkar,“ A Novel Approach of Classifying and Recognizing the Audio Scenario’s Profile”, International Journal of Computer Applications (0975 – 8887) National Conference on Advances in Computing (NCAC 2015)

Swati Sargule, Ramesh M Kagalkar” Hindi Language Document Summarization using Context Based Indexing Model”, CiiT International Journal of Data Mining Knowledge Engineering, Vol.08 No. 01, Jan Issue 2016.

Rachana Palaskar, Shweta Pandey, Ashwini Telang, Akshada Wagh, Ramesh Kagalkar,“ An Automatic Monitoring and Swing the Baby Cradle for Infant Care”, International Journal of Advanced Research in Computer and Communication Engineering,Vol. 4, Issue 12, December 2015.

Ramesh M Kagalkar, Kajal Chavan, Asmita Jadhav , Ravina Patil and Asmita Rawool, ” Self-Educating Tool Kit for Kids” CiiT Software Engineering and Technology, Vol 8, No 1, 2016.


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