@InProceedings{DavidAldavert2018,
author="David Aldavert
and Mar{\c{c}}al Rusi{\~n}ol",
title="Manuscript text line detection and segmentation using second-order derivatives analysis",
booktitle="13th IAPR International Workshop on Document Analysis Systems",
year="2018",
pages="293--298",
optkeywords="text line detection",
optkeywords="text line segmentation",
optkeywords="text region detection",
optkeywords="second-order derivatives",
abstract="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.",
optnote="DAG; 600.084; 600.129; 302.065; 600.121",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3104), last updated on Fri, 28 Jan 2022 09:35:14 +0100",
doi="10.1109/DAS.2018.24",
file=":http://refbase.cvc.uab.es/files/AlR2018a.pdf:PDF"
}