Line Segmentation from Unconstrained Handwritten Text Images using Adaptive Approach

TL;DR

This paper introduces an adaptive line segmentation method for handwritten text images that dynamically calculates text height, achieving high detection rates across complex and varied handwriting datasets.

Contribution

The work presents a novel adaptive approach that dynamically measures text height for improved robustness in handwritten line segmentation.

Findings

Achieved 98.01% detection rate on complex datasets.

Performed well on benchmark datasets IAM and ICDAR09.

Demonstrated robustness across diverse handwriting styles.

Abstract

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.