Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/4448
Title: Detection and segmentation of touching handwritten Gurmukhi script
Researcher: Rajiv, Kumar
Guide(s): Singh, Amardeep
Keywords: Segmentation
Natural Language
Automatic Document Processing
Character Recognition
Optical Character Recognition (OCR)
Computer Engineering
Upload Date: 31-Aug-2012
University: Punjabi University
Completed Date: July, 2011
Abstract: The scanned image of the text is not of any use for user, because that image is not editable. One can not make any change if required to the scanned document. This provides a food for thought for the theory of optical character recognition (OCR). OCR is nothing but character recognition of a segmented part of the scanned image. The overall OCR process consists of three major sub processes like pre processing, segmentation and then recognition. Out of these three, the segmentation process is the back bone of the overall OCR process. We can say that the segmentation process is the most significant process because if the segmentation is incorrect then we can not have the correct results; it is just like garbage in and garbage out. Therefore the segmented part of the image would be such that it should provide a close relation to the character to be recognized. Hence segmentation plays an important role in the OCR process. There are problems in segmentation process, but the degree of the problems varies from script to script, that is, the problem set for segmentation of the text written in a particular script may differ than the problem set for the text written in other scripts. The characteristics of the script, plays a significant role in deciding the segmentation points. But it is not an easy job, because segmentation is one of the complex processes. It is more difficult if the document is handwritten because in that case only few points are there which can be used to make segmentation. In the present study, we formulate an approach to segment the scanned document image. As per this approach, initially this considers the whole image as one large window. Then this large window is broken into less large windows giving lines, once the lines are identified then each window consisting of a line is used to find a word present in that line. For that purpose we used the concept of variable sized window, that is, the window whose size can be adjusted according to needs.
Pagination: ii, 144p.
URI: http://hdl.handle.net/10603/4448
Appears in Departments:University College of Engineering

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01_title.pdfAttached File21.78 kBAdobe PDFView/Open
02_certificate.pdf15.39 kBAdobe PDFView/Open
03_declaration.pdf15.38 kBAdobe PDFView/Open
04_acknowledgements.pdf21.26 kBAdobe PDFView/Open
05_index.pdf25.95 kBAdobe PDFView/Open
06_list of figures.pdf27.6 kBAdobe PDFView/Open
07_list of tables.pdf19.53 kBAdobe PDFView/Open
08_chapter 1.pdf194.54 kBAdobe PDFView/Open
09_chapter 2.pdf293.98 kBAdobe PDFView/Open
10_chapter 3.pdf298.43 kBAdobe PDFView/Open
11_chapter 4.pdf625.65 kBAdobe PDFView/Open
12_chapter 5.pdf73.72 kBAdobe PDFView/Open
13_chapter 6.pdf49.59 kBAdobe PDFView/Open
14_bibliography.pdf88.81 kBAdobe PDFView/Open
15_abstract.pdf14.58 kBAdobe PDFView/Open


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