Writing Order Recovery in Complex and Long Static Handwriting

TL;DR

This paper introduces a new system for recovering the writing order in complex, long static handwriting, improving accuracy and evaluation methods, and demonstrating competitive results across multiple datasets.

Contribution

A novel system for estimating writing order in complex static handwriting, with a new evaluation metric and publicly available code.

Findings

Effective cluster resolution in static handwriting

Improved accuracy in order recovery across datasets

Potential for enhanced velocity estimation applications

Abstract

The order in which the trajectory is executed is a powerful source of information for recognizers. However, there is still no general approach for recovering the trajectory of complex and long handwriting from static images. Complex specimens can result in multiple pen-downs and in a high number of trajectory crossings yielding agglomerations of pixels (also known as clusters). While the scientific literature describes a wide range of approaches for recovering the writing order in handwriting, these approaches nevertheless lack a common evaluation metric. In this paper, we introduce a new system to estimate the order recovery of thinned static trajectories, which allows to effectively resolve the clusters and select the order of the executed pen-downs. We evaluate how knowing the starting points of the pen-downs affects the quality of the recovered writing. Once the stability and sensitivity of the system is analyzed, we describe a series of experiments with three publicly available databases, showing competitive results in all cases. We expect the proposed system, whose code is made publicly available to the research community, to reduce potential confusion when the order of complex trajectories are recovered, and this will in turn make the trajectories recovered to be viable for further applications, such as velocity estimation.