Francisco Alvaro

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Automatic recognition of printed mathematical symbols is a fundamental problem for recognition of mathematical expressions. Several classification techniques has been previously used, but there are very few works that compare different classification techniques on the same database and with the same experimental conditions. In this work we have tested(More)
In this paper we define a bidimensional extension of Stochastic Context-Free Grammars for structure detection and segmentation of images of documents. Two sets of text classification features are used to perform an initial classification of each zone of the page. Then, the document segmentation is obtained as the most likely hypothesis according to a(More)
We consider the difficult problem of classifying spatial relationships between symbols and subexpressions in handwritten mathematical expressions. We first improve existing geometric features based on bounding boxes and center points, normalizing them using the distance between the centers of the two symbols or subexpressions in question. We then propose a(More)
In this work, a system for recognition of printed mathematical expressions has been developed. Hence, a statistical framework based on two-dimensional stochastic contextfree grammars has been defined. This formal framework allows to jointly tackle the segmentation, symbol recognition and structural analysis of a mathematical expression by computing its most(More)
Mathematical expression recognition is an active research field that is related to document image analysis and typesetting. Several approaches have been proposed to tackle this problem, and automatic methods for performance evaluation are required. Mathematical expressions are usually represented as a coded string like LTEX or MathML for evaluation purpose.(More)