Design And Implementation Of Character Recognition Algorithms. Image,

Benefiting from the research and progress of mathematical theories and the development of computer science, digital image processing technology is more and more widely used in various areas. With the ability to learn unknown entities through vehicles instead of human eyes, pattern recognition has great application perspects and becomes one important branch in image processing field. Due to wide application prospects, character recognition has been developing quickly, and is practically used in OCR and vehicle license plate recognition fields now. Whereas, the character recognition, which abides a certain background and must meet a desired goal, is difficult and is still under research.In this thesis, the plate character recognition sub system consists of original image preprocessing, elliptical plate positioning, extraction of character region, character segmentation and character recognition.In the process of image processing, global threshold method is applied to the gray image to get binary image, classify the binary image to several conditions due to concrete situation, morphological operation is used to eliminate small connected regions and other useless regions.In the process of elliptical plate extraction and character segmentation, direct least squared fitting method is applied to the edge of the ellipse to get geometric parameters of the ellipse, including coordinates of the centre point, the lengths of the long-axis and short-axis. Rotate the image using the line slope detecting by Hough transformation, do shear transformation and resizing transformation with geometric parameters getting by ellipse fitting. Get an approximate circular area as a result of a collection of geometric transformations. Extract rectangular character region by center of the ellipse and the shape characteristics. Analyze the merit and demerit of projection segmentation and segment each character by projection method while considering some prior knowledge.In the process of character recognition, several methods of eigen value selection is discussed and merit and demerit of each one is analyzed. In terms of template matching with penalty points method, selecting the middle point of the line segment of the line passing through the center point of the connected regions in the background which ends by the character region on two ends, as the penalty points is proposed. The principal component analysis recognition algorithm with intraclass scatter matrix as the generating matrix is designed and implemented. For BP neutral network, data format of input and output is designed, the number of input and output neurons, transfer function are determined, proper number of hidden layer neurons is selected though experiment. Each pattern recognition algorithm is tested by the sample data, analyze failed cases. One method which combines two pattern recognition methods with high success rate to make the result more reliable is proposed after analyzing and comparing the experiment result.