Research Of Character Recognition Technical In Printing Mathematic Expressions Recognition

This paper investigated the recognition of the symbols in mathematical expressions. With the development of science and technology nowadays, the researches on the mathematical expressions, which are composed of many rules of science and technology, can make the mathematical expressions be used in searches, and therefore improve the level of science and technology in literature. The automation of expressions input can solve the low efficiency which result from hand input. With the development of computer network, it become the common way to transfer information in network and to transform the pictures' presence form of the mathematical expressions which can save the room and increase the transfer rate and so on.This paper introduced the research status quo of mathematics expression in the world, the difficulty in research is establish a system of mathematics expression recognition in common currency and it is required in practice application. In this system, we disposed the symbol image in binary system number to ascertain the threshold value at first. It will import noise in the process of image creation, so we adopted a method with the image to flatness the noise. We also thinned the symbol image with the Hilditch arithmetic to obtain the clear topology structure. Because of the diversity of symbol magnitude in mathematics expression, it is not easy to recognize the symbol, so we disposed thesesymbols into uniform size, then use the method of framework-chain cord and according to the symbol structure characters such as the number and the position of hole, the number of end point, the numbers of angle and so on, we classify those 103 symbols which are familiar in mathematical expressions into eight classes and ten sub-class, and each sub-class corresponds to a neural network. The 13-dimention eigenvectors, include the 9 gridding characters and 4 crossing characters belong to figure image of the symbol by statistical, as the eigenvalue of the symbol then train and study these picked eigenvalue with the neural network, save as template at last. In this paper, the templates are in fixed standard (the magnitude and the picture contrast), we can recognize the symbol with template marching. But some special characters were recognized and researched substituted by other symbols due to the restriction of the computer coding, thus all these 103 symbols can be recognized by experiment at last.