Multi-Task Handwritten Document Layout Analysis

TL;DR

This paper introduces a neural network-based system for comprehensive handwritten document layout analysis, capable of identifying text baselines and classifying document zones, showing promising results across multiple datasets.

Contribution

The novel system performs both geometric and logical layout analysis of handwritten documents using neural networks, advancing the state-of-the-art in document analysis.

Findings

Competitive results on three datasets

Effective baseline detection and zone classification

Potential for improved handwritten document processing

Abstract

Document Layout Analysis is a fundamental step in Handwritten Text Processing systems, from the extraction of the text lines to the type of zone it belongs to. We present a system based on artificial neural networks which is able to determine not only the baselines of text lines present in the document, but also performs geometric and logic layout analysis of the document. Experiments in three different datasets demonstrate the potential of the method and show competitive results with respect to state-of-the-art methods.