  Text lines are important parts of handwritten document images and easier to
analyze by further applications. Despite recent progress in text line
detection, text line extraction from a handwritten document remains an unsolved
task. This paper proposes to use a fully convolutional network for text line
detection and energy minimization for text line extraction. Detected text lines
are represented by blob lines that strike through the text lines. These blob
lines assist an energy function for text line extraction. The detection stage
can locate arbitrarily oriented text lines. Furthermore, the extraction stage
is capable of finding out the pixels of text lines with various heights and
interline proximity independent of their orientations. Besides, it can finely
split the touching and overlapping text lines without an orientation
assumption. We evaluate the proposed method on VML-AHTE, VML-MOC, and
Diva-HisDB datasets. The VML-AHTE dataset contains overlapping, touching and
close text lines with rich diacritics. The VML-MOC dataset is very challenging
by its multiply oriented and skewed text lines. The Diva-HisDB dataset exhibits
distinct text line heights and touching text lines. The results demonstrate the
effectiveness of the method despite various types of challenges, yet using the
same parameters in all the experiments.
