02003nas a2200241   4500000000100000000000100001008004100002260001200043653001500055653003300070653002700103100001600130700001800146700002100164700002000185700002100205245006600226856008100292300001200373490000600385520135600391022001401747       2022                            d  c06/202210aClustering10aComplex and Long Handwriting10aWriting Order Recovery1 aMoises Diaz1 aGioele Crispo1 aAntonio Parziale1 aAngelo Marcelli1 aMiguel A. Ferrer00aWriting Order Recovery in Complex and Long Static Handwriting  uhttps://www.ijimai.org/journal/sites/default/files/2022-05/ijimai_7_4_15.pdf  a171-1840 v73 aThe 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 pendowns. 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.  a1989-1660