Text Line Segmentation for Challenging Handwritten Document Images Using Fully Convolutional Network

TL;DR

This paper introduces a Fully Convolutional Network-based method for segmenting challenging handwritten historical manuscript images, effectively handling complex layouts and touching components, with results comparable to previous methods.

Contribution

The paper demonstrates the effectiveness of FCN for text line segmentation in complex handwritten manuscripts, addressing challenges like curved, multi-skewed lines and touching components.

Findings

FCN-based approach performs well on challenging manuscript images.

The method is comparable to previous state-of-the-art results.

A new evaluation metric accounts for over- and under-segmentation.

Abstract

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.