Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/4166
Title: An investigation into Telugu font and character recognition
Researcher: Ghadiyaram, Anuradha
Guide(s): Agarwal, Arun
Keywords: Telugu Fonts
Character Recognition
Computer Science
Computer and Information Sciences
Upload Date: 9-Aug-2012
University: University of Hyderabad
Completed Date: 2009
Abstract: Optical Character Recognition (OCR) for Indian scripts and for handwriting is an active area of research. OCR systems for Indian and many other oriental languages are not yet able to successfully recognize printed documents of varying scripts, quality, font styls and sizes. To address some of these issues, we have designed and developed a framework to simultaneously recognize fonts and characters from documents printed in Telugu. Telugu script is structurally complicated due to the large number of character shapes that can be formed. Specifically, we developed a system based on Rough Sets theory for recognizing Telugu fonts and characters. Using the theory of rough sets, we determine the most important features of characters from the point of view of classification. The present work achieved 94.37%, and 90.17% accuracies for character and font recognitions respectively. These are the raw recognition accuracies without doing any post processing. The present model emphasizes the role of theory of rough sets in feature selection and dimension reduction in Telugu font and character recognition. A comparative study with related earlier works and our contributions to the field of Telugu character recognition are presented. The developed approach falls into the category of a priori classification, where domain knowledge of the content is not used. No explicit local feature analysis is used in this methodology. Applicability of the proposed approach is tested on documents printed in English, which is an entirely different script from Telugu. Thus, a general framework for script-independent character and font recognitions is suggestive from the present methodology. Encouraging recognition accuracies in both the scripts (Telugu and English) are achieved with a scope for further improvement.
Pagination: 192p.
URI: http://hdl.handle.net/10603/4166
Appears in Departments:Department of Computer & Information Sciences

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02_certificate.pdf52.46 kBAdobe PDFView/Open
03_declaration.pdf21.87 kBAdobe PDFView/Open
04_abstract.pdf23.79 kBAdobe PDFView/Open
05_acknowledgements.pdf23.53 kBAdobe PDFView/Open
06_contents.pdf37.44 kBAdobe PDFView/Open
07_list of figures.pdf23.53 kBAdobe PDFView/Open
08_list of tables.pdf23.47 kBAdobe PDFView/Open
09_chapter 1.pdf73.95 kBAdobe PDFView/Open
10_chapter 2.pdf863.02 kBAdobe PDFView/Open
11_chapter 3.pdf465.53 kBAdobe PDFView/Open
12_chapter 4.pdf4.37 MBAdobe PDFView/Open
13_chapter 5.pdf512.15 kBAdobe PDFView/Open
14_chapter 6.pdf108.96 kBAdobe PDFView/Open
15_chapter 7.pdf7.53 MBAdobe PDFView/Open
16_references.pdf67.06 kBAdobe PDFView/Open


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