Abstract
An OCR system is presented to understand mathematical formulas in binary printed document images. The system utilizes a novel component-labeling algorithm for extracting local maximum components from image, and uses these components to locate the mathematical formulas. A character recognition algorithm based on neural networks is then adopted. For segmenting merged characters in the image, a novel segmentation algorithm based on a modified SOM neural network was introduced into the system. With the employment of LL(1) grammar, this system can convert the recognition results into a \(\mbox{\LaTeX}\) file.
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Wu, W., Li, F., Kong, J., Hou, L., Zhu, B. (2006). A Bottom-Up OCR System for Mathematical Formulas Recognition. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_27
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DOI: https://doi.org/10.1007/11816157_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
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