Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/424094
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dc.coverage.spatial
dc.date.accessioned2022-12-12T05:12:42Z-
dc.date.available2022-12-12T05:12:42Z-
dc.identifier.urihttp://hdl.handle.net/10603/424094-
dc.description.abstractOnline handwriting recognition problem has been well known in pattern recognition and machine learning community for long. But, it is still a challenging newlinetask to recognize accurately the online handwritten texts written in various Indic newlinescripts like Devanagari, Bengali, Telugu and Tamil. These are four most popular Indic scripts. The works available in the literature on these Indic scripts newlinevary from one script to other. Regarding character level recognition, researchers newlinehave worked on Devanagari and Tamil scripts with both simple and compound newlinecharacter recognition but works in Bengali script are confined only within simple characters. Real time requirements are for texts having both characters as newlinewell as numerals. None of these scripts have been analyzed with numerals in newlinecombination with characters. The present research work proposes two different newlineapproaches - one without combining the outcomes of Support Vector Machine newline(SVM) and Hidden Markov Model (HMM) classifiers and the other by combining the outcomes of these two classifiers, to recognize both online handwritten newlinesimple and compound characters as well as numerals in Devanagari, Bengali and newlineTamil scripts. The present research work trains the system initially by generating newlineseparate training datasets for numerals, simple characters and compound characters and then a single training dataset for all these symbols. The first approach newlineproposes novel zone-based feature extraction approaches, one of which is used newlinein the second approach as well. Regarding isolated word level recognition, little newlinenumber of research works are available in Devanagari, Bengali and Tamil scripts, newlinebut works in Telugu script are confined within only character level recognition. newlineThe present work also proposes two different approaches to develop an online newlinehandwritten script identification and isolated word recognition system in different Indic scripts.
dc.format.extentxx, 144p.
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleOnline Handwritten Character and Word Recognition in Indic scripts
dc.title.alternative
dc.creator.researcherGhosh, Rajib
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Artificial Intelligence
dc.subject.keywordEngineering and Technology
dc.description.note
dc.contributor.guideKumar, Prabhat
dc.publisher.placePatna
dc.publisher.universityNational Institute of Technology Patna
dc.publisher.institutionComputer Science and Engineering
dc.date.registered2012
dc.date.completed2018
dc.date.awarded2020
dc.format.dimensions29cm.
dc.format.accompanyingmaterialCD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Computer Science and Engineering

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01_title.pdfAttached File141.1 kBAdobe PDFView/Open
02_prelim pages.pdf231.45 kBAdobe PDFView/Open
03_content.pdf97.53 kBAdobe PDFView/Open
04_abstract.pdf72.32 kBAdobe PDFView/Open
05_chapter 1.pdf318.15 kBAdobe PDFView/Open
06_chpater 2.pdf95.91 kBAdobe PDFView/Open
07_chapter 3.pdf380.72 kBAdobe PDFView/Open
08_chapter 4.pdf1.2 MBAdobe PDFView/Open
09_chapter 5.pdf813.96 kBAdobe PDFView/Open
10_chapter 6.pdf356.15 kBAdobe PDFView/Open
11_chapter 7.pdf1.6 MBAdobe PDFView/Open
12_chapter 8.pdf1.31 MBAdobe PDFView/Open
13_chapter 9.pdf71.51 kBAdobe PDFView/Open
14_annexures.pdf94.04 kBAdobe PDFView/Open
80_recommendation.pdf143.85 kBAdobe PDFView/Open


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