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.

从静态图像中恢复复杂且较长的手写轨迹的顺序是一项具有挑战性的任务，本文介绍了一种估算细化的静态轨迹顺序恢复的新系统，并通过一系列实验展示了竞争性结果，旨在为后续应用提供可行的轨迹恢复。