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Farsi handwritten databases and offline handwritten isolated digits recognition

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Farsi handwritten databases and offline handwritten isolated digits recognition

Solimanpour, Farshid (2007) Farsi handwritten databases and offline handwritten isolated digits recognition. Masters thesis, Concordia University.

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Abstract

This thesis describes an important step towards the standardization of the research on Optical Character Recognition (OCR) in Farsi language. It includes the development of several novel and standard Farsi handwritten databases, consisting of Farsi isolated digits, isolated letters, numerical strings, legal amounts on cheques, dates, and English isolated digits. Despite conventional research and an Internet search, to the best of our knowledge, no publicly accessible handwritten Farsi database exists that is available to researchers. In a character recognition system, three data sets are usually required: (1) Training set for training the classifier using designed features, (2) Verifying set for checking and adjusting the designed system, (3) Testing set to finally measure the performance of the system. To cover all the specified requirements, all our databases contain complete sets of training, testing, and verifying samples. Data entry forms were used for collecting handwritings. To process those forms, some form processing techniques were used to automate the process of extracting images of different fields in the forms, and to segment the numerical strings into isolated digits. Included in this thesis, is the implementation of a recognition system for recognizing our handwritten Farsi isolated digits database which may be used for comparison with the results of future research. For this recognition system, we used three feature sets including outer profiles, crossing counts and projection histograms; and for classification we used Support Vector Machines with an RBF kernel which gave us a recognition rate of 97.46% on our Testing Set. We also applied a rejection method to our system, which could improve the error rate by 1.18% by a rejection rate of 2.94%

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Solimanpour, Farshid
Pagination:xiii, 96 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science and Software Engineering
Date:2007
Thesis Supervisor(s):Suen, Ching Y.
ID Code:975259
Deposited By: Concordia University Library
Deposited On:22 Jan 2013 16:04
Last Modified:18 Jan 2018 17:39
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