Axial Representation of Character by Using Wavelet Transform
Axial representation plays a significant role in character recognition. The strokes of a character may consist of two regions, i.e. singular and regular regions. Therefore, a method to extract the central axis of a character requires two different processes to compute the axis in theses two different regions. The major problem of most traditional algorithms is that the extracted central axis in the singular region may be distorted by artifacts and branches. To overcome this problem, the wavelet-based amendment processing technique is developed to link the primary axis, so that the central axis in the singular region can be produced. Combining with our previously developed method for computing the primary axis in the regular region, we develop a novel scheme of extracting the central axis of character based on the wavelet transform (WT). Experimental results show that the final axis obtained from the proposed scheme closely resembles the human perceptions. It is applicable to both binary image and gray-level image as well. The axis representation is robust against noise.
KeywordsCharacteristic Point Wavelet Trans Corner Point Wavelet Function Modulus Maximum
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