The Design And Realization Of Printed Mathematical Expression Recognition System

The computerized document-handling systems have been widely used, but few systems have provided functions for recognizing and understanding mathematics expressions printed in document. The system proposed in this article has the ability to recognize mathematics expressions in files scanned directly from paper and to reconstruct the recognized expressions into particular publication format such as LATEX or WORD.The system works as follows :merged-symbol segmentation. Due to the quality of printer, cleanliness of paper, resolution of scanner, binarization etc., symbols in scanned document may be merged, therefore, can not be easily recognized. In this article, we proposed a new method, self-organizing feature map, to segment merged-symbol. By modifying the classic updating rule of self-organizing map, we obtained a network that can approximate the distribution of white-pixels between two symbols in less training time and with less units.feature extraction and selection. A symbol in image file can not be classified directly, cause it is not invariant with respect to image translation, orientation and size changes. In this article, we investigated three kinds of moment features that used as a shape descriptor: regular moments, Zernike moments and B-spline wavelet moments. We also used PCA neural network to select principal features, which reduced dimensions of feature space while retaining useful information.character recognition. Recognizer is key part in our system. Neural networks, which overcome the disadvantages of traditional pattern recognition methods, have been used extensively on OCR and have achieved higher recognition rate. In this article, we used SOFM network as rough-classifier, which classify similar symbols into same group. After that, we used BP network as fine-classifier, which identified symbols within one group.expression formation. So far, the problem of understanding a complicated mathematics expressions in a printed document has not been completely solved yet. We introduced a formation algorithm for locating the superscript and subscript, and for analyzing the two-dimensional layout structure of the symbols within a expression. Then the structure of a recognized expression was represented by a tree structure and the original expression could be reproduced by using a suitable formatter like LATEX.The experimental results at the end of article have demonstrated the feasibility of the system. But the model we proposed still needs further improvement for commercial application.