Knowledge based text character recognition using Fourier transform

The Fourier transformation was applied on a set of typed text characters, extracting their unique features and developing an appropriate knowledge base for quick text character recognition. The use of this technique may also allow the development of an adaptive recognizer capable of learning through proper development of the classifier. The proposed technique computes the Fourier transform of the input string derived by the HVP (horizontal-vertical projection) process. In particular, the string created by the HVP scheme is a combination of two strings from the horizontal and vertical projections. The coefficients of the input string-derived Fourier series are compared with the features of the known characters, and classification is performed based on the closeness of the features set. Analysis of test results showed that the Fourier transform approach for feature extraction and the simple classification technique chosen in this project displayed a classification accuracy of over 80% for a limited set of conditions