Abstract
This paper presents an effective method for writer recognition in handwritten documents. We have introduced a set of features that are extracted from two different representations of the contours of handwritten images. These features mainly capture the orientation and curvature information at different levels of observation, first from the chain code sequence of the contours and then from a set of polygons approximating these contours. Two writings are then compared by computing the distances between their respective features. The system trained and tested on a data set of 650 writers exhibited promising results on writer identification and verification.
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Siddiqi, I., Vincent, N. (2009). Combining Contour Based Orientation and Curvature Features for Writer Recognition. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_30
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DOI: https://doi.org/10.1007/978-3-642-03767-2_30
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