  Handwritten Text Line Segmentation (HTLS) is a low-level but important task
for many higher-level document processing tasks like handwritten text
recognition. It is often formulated in terms of semantic segmentation or object
detection in deep learning. However, both formulations have serious
shortcomings. The former requires heavy post-processing of splitting/merging
adjacent segments, while the latter may fail on dense or curved texts. In this
paper, we propose a novel Line Counting formulation for HTLS -- that involves
counting the number of text lines from the top at every pixel location. This
formulation helps learn an end-to-end HTLS solution that directly predicts
per-pixel line number for a given document image. Furthermore, we propose a
deep neural network (DNN) model LineCounter to perform HTLS through the Line
Counting formulation. Our extensive experiments on the three public datasets
(ICDAR2013-HSC, HIT-MW, and VML-AHTE) demonstrate that LineCounter outperforms
state-of-the-art HTLS approaches. Source code is available at
https://github.com/Leedeng/Line-Counter.
