  Line segmentation from handwritten text images is one of the challenging task
due to diversity and unknown variations as undefined spaces, styles,
orientations, stroke heights, overlapping, and alignments. Though abundant
researches, there is a need of improvement to achieve robustness and higher
segmentation rates. In the present work, an adaptive approach is used for the
line segmentation from handwritten text images merging the alignment of
connected component coordinates and text height. The mathematical justification
is provided for measuring the text height respective to the image size. The
novelty of the work lies in the text height calculation dynamically. The
experiments are tested on the dataset provided by the Chinese company for the
project. The proposed scheme is tested on two different type of datasets;
document pages having base lines and plain pages. Dataset is highly complex and
consists of abundant and uncommon variations in handwriting patterns. The
performance of the proposed method is tested on our datasets as well as
benchmark datasets, namely IAM and ICDAR09 to achieve 98.01% detection rate on
average. The performance is examined on the above said datasets to observe
91.99% and 96% detection rates, respectively.
