An efficient low-level word image representation plays a crucial role in general cursive word recognition. This paper proposes a novel representation scheme, where a word image can be represented as two sequences of feature vectors in two independent channels, which are extracted from vertical peak points on the upper external contour and at vertical minima on the lower external contour, respectively. A data-driven method based on support vector machine is applied to prune and group those extreme points. Our experimental results look promising and have indicated the potential of this low-level representation for complete cursive handwriting recognition. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Dong, J. X., Krzyzak, A., Suen, C. Y., & Ponson, D. (2005). Low-level cursive word representation based on geometric decomposition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3587 LNAI, pp. 590–599). Springer Verlag. https://doi.org/10.1007/11510888_58
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