Text line extraction using fully convolutional network and energy minimization

TL;DR

This paper introduces a novel approach combining fully convolutional networks and energy minimization to accurately detect and extract handwritten text lines with arbitrary orientations, overlaps, and varying heights.

Contribution

It presents a new method that effectively detects and extracts complex handwritten text lines without orientation assumptions, outperforming previous techniques.

Findings

Effective detection of arbitrarily oriented text lines.

Accurate extraction of overlapping and touching lines.

Consistent performance across diverse datasets.

Abstract

Text lines are important parts of handwritten document images and easier to analyze by further applications. Despite recent progress in text line detection, text line extraction from a handwritten document remains an unsolved task. This paper proposes to use a fully convolutional network for text line detection and energy minimization for text line extraction. Detected text lines are represented by blob lines that strike through the text lines. These blob lines assist an energy function for text line extraction. The detection stage can locate arbitrarily oriented text lines. Furthermore, the extraction stage is capable of finding out the pixels of text lines with various heights and interline proximity independent of their orientations. Besides, it can finely split the touching and overlapping text lines without an orientation assumption. We evaluate the proposed method on VML-AHTE, VML-MOC, and Diva-HisDB datasets. The VML-AHTE dataset contains overlapping, touching and close text lines with rich diacritics. The VML-MOC dataset is very challenging by its multiply oriented and skewed text lines. The Diva-HisDB dataset exhibits distinct text line heights and touching text lines. The results demonstrate the effectiveness of the method despite various types of challenges, yet using the same parameters in all the experiments.