PT Unknown
AU David Aldavert
   Marçal Rusiñol
TI Manuscript text line detection and segmentation using second-order derivatives analysis
BT 13th IAPR International Workshop on Document Analysis Systems
PY 2018
BP 293
EP 298
DI 10.1109/DAS.2018.24
DE text line detection; text line segmentation; text region detection; second-order derivatives
AB 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.
ER