Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/512477
Title: A hybrid framework for tamil handwritten character recognition system using deep learning approach
Researcher: Babitha Lincy, R
Guide(s): Gayathri, R
Keywords: deep learning
Engineering
Engineering and Technology
Engineering Electrical and Electronic
hybrid framework
tamil handwritten
University: Anna University
Completed Date: 2022
Abstract: Optical Character Recognition (OCR) is one of the growing newlinetechnologies in the data processing section. The objective of Optical newlineCharacter Recognition is defined as identifying and understanding the data newlinepresented on paper or digital documents by the system as humans do. Various newlineattempts and research have been carried out in this realm. The success of newlineLatin script recognition opened the research publically, worldwide. According newlineto the Indian research, work-related to Hindi language recognition got a lot of newlineattention, since it is the national language of India. But till date no one could newlinebeat the growth of the Latin script by other non-Latin scripts, in terms of newlineaccuracy rate and successful implementations. This thesis will reveal the newlineterms presented in character recognition in Tamil, one of the popular south newlineIndian languages.Tamil is one of the oldest Dravidian languages with a strong traditional newlinehistory. Tamil character recognition has two categories such as printed newlinecharacter recognition and handwritten character recognition. Handwritten newlinecharacter recognition can be further classified into two types such as online newlineand offline recognition. During the online identification of Tamil handwritten newlinescript, the OCR engine will acknowledge the written characters spontaneously newlinewhile writing using a digital pen, transducer, and touch screen. But the offline newlineidentification of Tamil handwritten script is just opposite to the online newlineidentification. In this offline recognition, the handwritten Tamil characters are newlineidentified later at the end of the writing and scanning process. Offline Tamil newlineHandwritten Character Recognition (THCR) is characterized as the automatic newlineidentification of Tamil isolated symbols or words from the scanned image newlinedocument. Moreover, the thesis is going to study the work regarding offline newlineTamil handwritten character identification. However, the offline THCR is newlinemore difficult than the online Tamil handwritten script identification. A newlinecritical challenge in handwritten symbol identification is to work with the vast newlinevariability of handwriting styles for the same symbols of the Tamil language. newlineTherefore, the thesis will discuss the algorithms for Tamil handwritten newlinecharacter recognition and the challenges related to the implementation of newlinethese algorithms. In this exploration, three different deep learning strategies newlineare planned for classification applications. In the first strategy, an advanced newlinetransfer learning technique is used with deep learning models newline newline
Pagination: xviii,161p.
URI: http://hdl.handle.net/10603/512477
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File34.9 kBAdobe PDFView/Open
02_prelim pages.pdf3.16 MBAdobe PDFView/Open
03_content.pdf7.78 kBAdobe PDFView/Open
04_abstract.pdf9.17 kBAdobe PDFView/Open
05_chapter 1.pdf454.53 kBAdobe PDFView/Open
06_chapter 2.pdf241.42 kBAdobe PDFView/Open
07_chapter 3.pdf1.31 MBAdobe PDFView/Open
08_chapter 4.pdf701.04 kBAdobe PDFView/Open
09_chapter 5.pdf692.23 kBAdobe PDFView/Open
10_chapter 6.pdf735 kBAdobe PDFView/Open
11_annexures.pdf103.02 kBAdobe PDFView/Open
80_recommendation.pdf81.73 kBAdobe PDFView/Open
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