Abstract
Automatic recognition of handwritten mathematical expressions in Arabic is a difficult problem, even if all the symbols that compose the expression are recognized correctly. The classification of spatial relations between pairs of adjacent symbols is a key problem as this classification often determines the semantic interpretation of an expression. In this work, we propose a system for the identification of spatial relationships based on geometric features and a new descriptor named spatial histogram. After features extraction, two types of fusion are compared for the final classification which are: feature-level fusion and decision-level fusion. In our proposed system, a support vector machine (SVM) classifier is employed. Experimental results show that our features give promising results. Moreover, using the decision-level fusion improve the classification accuracy.
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Ali, I.H., Mahjoub, M.A. (2021). Identification of Spatial Relationships in Arabic Handwritten Expressions Using Multiple Fusion Strategies. In: Balas, V., Jain, L., Balas, M., Shahbazova, S. (eds) Soft Computing Applications. SOFA 2018. Advances in Intelligent Systems and Computing, vol 1222. Springer, Cham. https://doi.org/10.1007/978-3-030-52190-5_22
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DOI: https://doi.org/10.1007/978-3-030-52190-5_22
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