A lot of research have been carried in the field of optical character recognition. Selection of
a feature extraction scheme is probably the most important factor in achieving high recognition
performance. Fourier and wavelet transforms are among the popular feature extraction
techniques allowing rotation invariant recognition. The performance of a particular feature
extraction technique depends on the used dataset and the classifier. Dierent feature types
may need dierent types of classifiers. In this thesis Fourier and wavelet based features are
compared in terms of classification accuracy. The influence of noise with dierent intensities
is also analyzed. Character recognition system is implemented with Matlab. Isolated gray
scale character image first transformed into one dimensional function. Then, set of features
are extracted. The feature set are fed to a classifier. Two types of classifier were used, Nearest
Neighbor and Linear Discriminant Function. The performance of each feature extraction and
classification methods were tested on various rotated and scaled character images.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12612928/index.pdf |
| Date | 01 January 2011 |
| Creators | Onak, Onder Nazim |
| Contributors | Oktem, Hakan |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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