Multi-font and multi-size character recognition based on the sampling and quantization of an unwrapped contour

We propose a character recognition method based on the sampling and quantization of unwrapped contour information. The character contour includes many different features. Numerous approaches are explored to find essential features which are extracted directly from the contour. Our idea is to decompose the multi-dimensional features of a contour into one dimensional features. Following the contour, we extract horizontal, vertical, and angular variations and make unwrapped contours from these features. Unwrapped contour information is sensitive to the starting point of contour tracing, and it varies on the font type and size, so we propose a novel algorithm which finds the invariant starting point of contour tracing. By sampling and quantization, we can make discrete contours which are invariant to the font and size