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Goyal, Lalit
- ISL (Indian Sign Language) Dictionary using Synthetic Animations (Extension to Existing)
Abstract Views :396 |
PDF Views:1
Authors
Annu Rani
1,
Lalit Goyal
2
Affiliations
1 Research Scholar, Department of Computer Science, Punjabi University, Patiala (Punjab), IN
2 Department of Computer Science, DAV College, Jalandhar (Punjab), IN
1 Research Scholar, Department of Computer Science, Punjabi University, Patiala (Punjab), IN
2 Department of Computer Science, DAV College, Jalandhar (Punjab), IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 34 (2021), Pagination: 6-13Abstract
This paper presents extended vocabulary in ISL dictionary using synthetic animations which uses virtual character instead of a real human. Unlike other spoken languages such as English, Hindi, Bengali, Punjabi Urdu, etc., sign language cannot be written. HamNoSys is an international code system that is used to write any sign language. This HamNoSys code is then translated into SiGML code by using a third-party tool. At last Sigml player generates the synthetic animation. This dictionary can be used by special educators while teaching ISL to deaf students. This synthetic animation dictionary can be used for a conversion system in which written or spoken text can be translated into ISL animations.Keywords
Communication, Deaf Community, Dictionary, ISL, Sign Language.References
- C. J. Sruthi and A. Lijiya, “SigNet: A deep learning based indian sign language recognition system,” Proc. 2019 IEEE Int. Conf. Commun. Signal Process. ICCSP 2019, pp. 596–600, 2019, doi: 10.1109/ICCSP.2019.8698006.
- A. V.Nair and B. V, “A Review on Indian Sign Language Recognition,” Int. J. Comput. Appl., vol. 73, no. 22, pp. 33–38, 2013, doi: 10.5120/13037-0260.
- S. Kaur and M. Singh, “Indian Sign Language animation generation system,” Proc. 2015 1st Int. Conf. Next Gener. Comput. Technol. NGCT 2015, no. September, pp. 909–914, 2016, doi: 10.1109/NGCT.2015.7375251.
- Vishwambhar Singh, “Hearing in India: All aspects,” Otolaryngol. Online J., vol. 5, no. 1, pp. 1–31, 2015, [Online]. Available: http://jorl.net/index.php/jorl/article/viewFile/342/pdf_132.
- D. S. Sharma et al., “Automatic Translation of English Text to Indian Sign Language Synthetic Animations,” NLP Assoc. India, no. December, pp. 144–153, 2016.
- J. Joy and K. Balakrishnan, “A prototype Malayalam to Sign Language Automatic Translator,” pp. 2000–2002, 2014, [Online]. Available: http://arxiv.org/abs/1412.7415.
- L. Goyal and V. Goyal, “Development of Indian Sign Language Dictionary using Synthetic Animations,” Indian J. Sci. Technol., vol. 9, no. 32, 2016, doi: 10.17485/ijst/2016/v9i32/100729.
- “History of Handspeak.” https://www.handspeak.com/word/search/index.php?id=1036.
- “Basic Words Dictionary in ASL.” A Basic Guide to ASL (masterstech-home.com) (accessed Apr. 25, 2021).
- “Signing Savvy.” https://www.signingsavvy.com/features (accessed Apr. 25, 2021).
- “Spread The Signs.” https://www.spreadthesign.com/isl.intl/search/ (accessed Apr. 23, 2021).
- J. Fenlon, K. Cormier, and A. Schembri, “Building BSL SignBank: The lemma dilemma revisited,” Int. J. Lexicogr., vol. 28, no. 2, pp. 169–206, 2015, doi: 10.1093/ijl/ecv008.
- “Mook Badhir Mandal.” http://indiandeaf.org (accessed Apr. 24, 2021).
- “ISL Portal.” https://indiansignlanguage.org/ (accessed Apr. 24, 2021).
- “ISL Dictionary.” http://www.islrtc.nic.in/isl-dictionary-launch (accessed Apr. 21, 2021).
- T. Hanke, “HamNoSys – Alphabet with a symbol inventory of,” 2009.
- R. Kaur and P. Kumar, “HamNoSys generation system for sign language,” Proc. 2014 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2014, pp. 2727–2734, 2014, doi: 10.1109/ICACCI.2014.6968333.
- Automatic Extraction of Idiom, Proverb and its Variations from Text using Statistical Approach
Abstract Views :209 |
PDF Views:0
Authors
Chitra Garg
1,
Lalit Goyal
2
Affiliations
1 Department of Computer Science, Banasthali University, Rajasthan, IN
2 Department of Computer Science, D.A.V. College, Jalandhar, Punjab, IN
1 Department of Computer Science, Banasthali University, Rajasthan, IN
2 Department of Computer Science, D.A.V. College, Jalandhar, Punjab, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 10 (2014), Pagination: 12-17Abstract
Natural languages are full of idiomatic uses, which while translating through present NLP system do not extract variations of idioms and proverbs. To overcome this problem, a new method to extract idioms/proverbs is proposed in this paper. The proposed methodology uses statistical method to automatically extract idioms and proverbs from the text along with their variations. The system is updated with a huge database of idioms and proverbs with all of their variations and then tested on a large text file of 'Panchatantra Tales'. The system gave an accuracy of more than 80%, which proves that our method is a successful approach in correctly interpreting and generating the translation of natural language.Keywords
Natural Language, Proverb, Idiom, Statistical Approach, Idiomatic.- n-Gram Character Analysis of English Text on Domain Specific Corpus
Abstract Views :184 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, DAV College, Jalandhar, IN
1 Department of Computer Science, DAV College, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 9 (2013), Pagination: 44-48Abstract
Statistical analysis of a language is a vital part of natural language processing. It refers to a collection of methods used to process large amounts of data and report overall trends. In this paper, frequency and word length analysis of individual characters in English text is performed. Unigram, bigram, trigram and positional analysis characters in the domain specific English corpus in health domain has been studied. Miscellaneous analysis like Percentage occurrence of various numbers of distinct words and their coverage in English Corpus is studied.Keywords
Corpus, English, Statistical Analysis, Quantitative Analysis, Unigram, Bigram, Trigram.- Quantitative Analysis of English Corpus in Tourism and Health Domain
Abstract Views :173 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, DAV College, Jalandhar, IN
1 Department of Computer Science, DAV College, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 8 (2013), Pagination: 92-97Abstract
Statistical analysis of a language is an essential part of any of the natural language processing activity though it is translation, transliteration, summarization, lexicon formation, keyboard designs and many more. In this paper, a domain specific corpus (health and tourism) of English language provided by Computational Linguistic R & D at Special Centre for Sanskrit Studies J.N.U is analyzed statistically. The frequency analysis and word length analysis of English text is performed. Unigram, bigram, trigram and positional analysis of words has been studied.Keywords
Corpus, English, Statistical Analysis, Quantitative Analysis, Unigram, Bigram, Trigram Introduction.- Translation of English Complex/Compound Sentences into Indian Sign Language
Abstract Views :329 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Punjabi University, Patiala (Punjab), IN
2 Department of Computer Science, DAV College, Jalandhar (Punjab), IN
1 Department of Computer Science, Punjabi University, Patiala (Punjab), IN
2 Department of Computer Science, DAV College, Jalandhar (Punjab), IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 33 (2020), Pagination: 1-14Abstract
.This paper outlines the concept for translation of English text to Indian Sign Language using real-domain synthetic animations. The translation framework consists of a processing module that parses the English input sentence to phrase structure grammar representation on which Indian sign language grammar rules are applied to reorder English phrase terms. The input English sentences was given by the user are parsed through the converter module which in turn exchange the complex and compound English sentences to their simplify versions by means of complex to simple and compound to simple English grammar rules. Elimination module removes unacceptable terms from the reordered sentence. Lemmatization is done in order to translate the words into the ischolar_main form since the Indian sign language does not use the word inflections. All the words in the sentence are then tested into a lexicon containing the English word and its HamNoSys notation and their synonym replaces the terms that are not in the lexicon. Sentence words are substituted with their HamNoSys counter code. In the event that the word is not in the lexicon, HamNoSys code is taken for each word alphabet. The HamNoSys data is translated to the SiGML tags, and these SiGML tags are sent to animation module which translates the SiGML code to the synthetic animation utilizing avatar.Keywords
Indian Sign language ,HamNoSys,SiGMLReferences
- Anuja, K. & Suryapriya, S. & Idicula, Sumam. (2010). Design and development of a frame based MT system for English-to-ISL: World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings. 1382 - 1387. 10.1109/NABIC.2009.53 93721.
- Anuja V Nair and Bindu V. (2013) Article: A Review on Indian Sign Language Recognition. International Journal o f Computer Applications 73(22):33-38, July 2013.
- Biplav Sarma, Anup Kumar Barman, (2015), A Comprehensive Survey of Noun Phrase Chunking in Natural Languages, International Journal Of Engineering Research & Technology (IJERT) Volume 04, Issue 04 (April 2015), http://dx.doi.org/10.17577/IJERTV4IS040854
- Dasgupta, T., & Basu, A. (2008). Prototype machine translation system from text-to-Indian sign language. In Proceedings o f the 13th international conference on Intelligent user interfaces (pp. 313-316).
- Goyal, L., & Goyal, V. (2016). Automatic translation of English text to Indian sign language synthetic animations in Proceedings o f the 13th International Conference on Natural Language Processing ICON (pp. 144-153).
- Goyal, L., & Goyal, V. (2017). Tutorial for Deaf-Teaching Punjabi Alphabet using Synthetic Animations. In Proceedings o f the 14th International Conference on Natural Language Processing (ICON-2017) (pp. 172-177).
- Narula, R., & Sharma, S. K. (2014). Identification and separation of simple, compound and complex sentences in Punjabi language. International Journal o f Computer Applications & Information Technology, 6.
- Pawan Kumar & Savita Khatri (2016) Generating Indian Sign Language Text Using English/Hindi Text, Special Issue: Conscientious and Unimpeachable Technologies International Journal o f Recent Research Aspects ISSN: 2349-7688, 2016, pp. 30-33
- R. Kaur & P. Kumar (2014) HamNoSys generation system for sign language, International Conference on Advances in Computing, Communications and Informatics (ICACCI), New Delhi, 2014, pp. 2727-2734, doi: 10.1109/ICACCI.2014.6968333.
- S.K Sharma (2019) Sentence Reduction for Syntactic Analysis of Compound Sentences in Punjabi Language. EAIEndorsed Transactions on Scalable Information Systems, 6 (20).
- Verma, D.A., & Kaur, S. (2015). Indian Sign Language Animation Generation System for Gurumukhi Script. IJCST ISSN: 0976-8491 (Online) | ISSN: 2229-4333 (Print) Vol. 6, Issue 3, July - Sept 2015p 117-121