  This paper presents a method for text line segmentation of challenging
historical manuscript images. These manuscript images contain narrow interline
spaces with touching components, interpenetrating vowel signs and inconsistent
font types and sizes. In addition, they contain curved, multi-skewed and
multi-directed side note lines within a complex page layout. Therefore,
bounding polygon labeling would be very difficult and time consuming. Instead
we rely on line masks that connect the components on the same text line. Then
these line masks are predicted using a Fully Convolutional Network (FCN). In
the literature, FCN has been successfully used for text line segmentation of
regular handwritten document images. The present paper shows that FCN is useful
with challenging manuscript images as well. Using a new evaluation metric that
is sensitive to over segmentation as well as under segmentation, testing
results on a publicly available challenging handwritten dataset are comparable
with the results of a previous work on the same dataset.
