Evaluation of feature extraction methods for numerals recognition

Wong, Justin (1999). Evaluation of feature extraction methods for numerals recognition B.Sc Thesis, School of Computer Science and Electrical Engineering, The University of Queensland.

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Author Wong, Justin
Thesis Title Evaluation of feature extraction methods for numerals recognition
School, Centre or Institute School of Computer Science and Electrical Engineering
Institution The University of Queensland
Publication date 1999
Thesis type B.Sc Thesis
Supervisor Dr. P.N.Suganthan
Ajantha. S. Atukorale
Total pages 33
Language eng
Subjects 0906 Electrical and Electronic Engineering
Formatted abstract

This thesis is about the evaluation of feature extraction methods for numerals recognition. Three extraction methods are designed and tested. The unconstrained numeral images are imported from the image database. The set of 128 x 128 bits raw images from the database, which consisted large variety digits written by different authors will be used.

The image needed to go through the pre-processing and normalisation phase before they are extracted with different methods. With pre-processing and normalisation process undergo, the image would be easier to recognise.

The three extraction methods are evaluated and compared with the speed and performance of the extraction. The entire image processing procedure including feature extraction are coded and implemented in C. The extracted feature points will be sent to a neural network that capable of recognising numeral character. The Matlab’s neural network toolbox was employed to implement the training part of the back-propagation network. Feature extraction methods will be tested with combination of different preprocessing and normalisation techniques. Testing and modifying the extraction methods will undergo during experiment, so the best feature extraction method will be evaluated.

Keyword Numerals recognition
Additional Notes * 4th year electrical engineering theses and information technology abstracts. 1999

Document type: Thesis
Collection: UQ Theses (non-RHD) - UQ staff and students only
Citation counts: Google Scholar Search Google Scholar
Created: Fri, 31 May 2013, 10:42:11 EST by Mr Yun Xiao on behalf of Scholarly Communication and Digitisation Service