%0 Conference Proceedings
%T Manuscript text line detection and segmentation using second-order derivatives analysis
%A David Aldavert
%A Marçal Rusiñol
%B 13th IAPR International Workshop on Document Analysis Systems
%D 2018
%F David Aldavert2018
%O DAG; 600.084; 600.129; 302.065; 600.121
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3104), last updated on Fri, 28 Jan 2022 09:35:14 +0100
%X In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over abright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets.
%K text line detection
%K text line segmentation
%K text region detection
%K second-order derivatives
%U http://refbase.cvc.uab.es/files/AlR2018a.pdf
%U http://dx.doi.org/10.1109/DAS.2018.24
%P 293-298