http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Divakar Yadav (검색결과 7 건)
- 무료
- 기관 내 무료
- 유료
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Optical Character Recognition for Hindi Language Using a Neural-network Approach
Yadav, Divakar,Sanchez-Cuadrado, Sonia,Morato, Jorge Korea Information Processing Society 2013 Journal of information processing systems Vol.9 No.1
Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.
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Goyal, Mukta,Yadav, Divakar,Tripathi, Alka Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.1
In this paper, Atanassov's intuitionistic fuzzy set theory is used to handle the uncertainty of students' knowledgeon domain concepts in an E-learning system. Their knowledge on these domain concepts has been collected from tests that were conducted during their learning phase. Atanassov's intuitionistic fuzzy user model is proposed to deal with vagueness in the user's knowledge description in domain concepts. The user model uses Atanassov's intuitionistic fuzzy sets for knowledge representation and linguistic rules for updating the user model. The scores obtained by each student were collected in this model and the decision about the students' knowledge acquisition for each concept whether completely learned, completely known, partially known or completely unknown were placed into the information table. Finally, it has been found that the proposed scheme is more appropriate than the fuzzy scheme.
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( Mukta Goyal ),( Divakar Yadav ),( Alka Tripathi ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.1
In this paper, Atanassov`s intuitionistic fuzzy set theory is used to handle the uncertainty of students` knowledgeon domain concepts in an E-learning system. Their knowledge on these domain concepts has been collected from tests that were conducted during their learning phase. Atanassov`s intuitionistic fuzzy user model is proposed to deal with vagueness in the user`s knowledge description in domain concepts. The user model uses Atanassov`s intuitionistic fuzzy sets for knowledge representation and linguistic rules for updating the user model. The scores obtained by each student were collected in this model and the decision about the students` knowledge acquisition for each concept whether completely learned, completely known, partially known or completely unknown were placed into the information table. Finally, it has been found that the proposed scheme is more appropriate than the fuzzy scheme.
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An Improved Approach to Ranking Web Documents
Gupta, Pooja,Singh, Sandeep K.,Yadav, Divakar,Sharma, A.K. Korea Information Processing Society 2013 Journal of information processing systems Vol.9 No.2
Ranking thousands of web documents so that they are matched in response to a user query is really a challenging task. For this purpose, search engines use different ranking mechanisms on apparently related resultant web documents to decide the order in which documents should be displayed. Existing ranking mechanisms decide on the order of a web page based on the amount and popularity of the links pointed to and emerging from it. Sometime search engines result in placing less relevant documents in the top positions in response to a user query. There is a strong need to improve the ranking strategy. In this paper, a novel ranking mechanism is being proposed to rank the web documents that consider both the HTML structure of a page and the contextual senses of keywords that are present within it and its back-links. The approach has been tested on data sets of URLs and on their back-links in relation to different topics. The experimental result shows that the overall search results, in response to user queries, are improved. The ordering of the links that have been obtained is compared with the ordering that has been done by using the page rank score. The results obtained thereafter shows that the proposed mechanism contextually puts more related web pages in the top order, as compared to the page rank score.
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An Improved Approach to Ranking Web Documents
Pooja Gupta,Sandeep K. Singh,Divakar Yadav,A. K. Sharma 한국정보처리학회 2013 Journal of information processing systems Vol.9 No.2
Ranking thousands of web documents so that they are matched in response to a user query is really a challenging task. For this purpose, search engines use different ranking mechanisms on apparently related resultant web documents to decide the order in which documents should be displayed. Existing ranking mechanisms decide on the order of a web page based on the amount and popularity of the links pointed to and emerging from it. Sometime search engines result in placing less relevant documents in the top positions in response to a user query. There is a strong need to improve the ranking strategy. In this paper, a novel ranking mechanism is being proposed to rank the web documents that consider both the HTML structure of a page and the contextual senses of keywords that are present within it and its back-links. The approach has been tested on data sets of URLs and on their back-links in relation to different topics. The experimental result shows that the overall search results, in response to user queries, are improved. The ordering of the links that have been obtained is compared with the ordering that has been done by using the page rank score. The results obtained thereafter shows that the proposed mechanism contextually puts more related web pages in the top order, as compared to the page rank score
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