Unsupervised Deep Learning for Handwritten Page Segmentation

TL;DR

This paper introduces an unsupervised deep learning approach for segmenting handwritten document images into regions with similar patterns, eliminating the need for human-labeled training data.

Contribution

It presents a novel siamese neural network-based method that differentiates patches using measurable properties, enabling effective page segmentation without annotations.

Findings

Achieves comparable accuracy to supervised methods

Effective on complex handwritten layouts

Reduces human labeling effort

Abstract

Segmenting handwritten document images into regions with homogeneous patterns is an important pre-processing step for many document images analysis tasks. Hand-labeling data to train a deep learning model for layout analysis requires significant human effort. In this paper, we present an unsupervised deep learning method for page segmentation, which revokes the need for annotated images. A siamese neural network is trained to differentiate between patches using their measurable properties such as number of foreground pixels, and average component height and width. The network is trained that spatially nearby patches are similar. The network's learned features are used for page segmentation, where patches are classified as main and side text based on the extracted features. We tested the method on a dataset of handwritten document images with quite complex layouts. Our experiments show that the proposed unsupervised method is as effective as typical supervised methods.