TY  - CONF
AU  - David Aldavert
AU  - Marçal Rusiñol
A2  - DAS
PY  - 2018//
TI  - Manuscript text line detection and segmentation using second-order derivatives analysis
BT  - 13th IAPR International Workshop on Document Analysis Systems
SP  - 293
EP  - 298
KW  - text line detection
KW  - text line segmentation
KW  - text region detection
KW  - second-order derivatives
N2  - 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.
L1  - http://refbase.cvc.uab.es/files/AlR2018a.pdf
UR  - http://dx.doi.org/10.1109/DAS.2018.24
N1  - DAG; 600.084; 600.129; 302.065; 600.121
ID  - David Aldavert2018
ER  -