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Writing Order Recovery in Complex and Long Static Handwriting

2024-06-05 12:23:17
Moises Diaz, Gioele Crispo, Antonio Parziale, Angelo Marcelli, Miguel A. Ferrer


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.

Abstract (translated)

轨迹执行的顺序是识别器的重要信息来源。然而,从静态图像中恢复复杂和长手写轨迹仍然没有一种通用的方法。复杂样品可能会导致多次 pen-down,产生大量轨迹交叉点,形成像素聚类(也称为簇)。虽然科学文献描述了从手写中恢复书写顺序的多种方法,但这些方法仍然缺乏一个共同的评估指标。在本文中,我们引入了一个新的系统来估计薄静态轨迹的恢复顺序,这使得有效地解决簇并选择执行的 pen-down 的顺序成为可能。我们评估了知道 pen-down 起点如何影响恢复书写顺序的质量。一旦系统的稳定性和敏感性被分析,我们在三个公开可用的数据库上进行了一系列实验,展示了所有情况下的 competitive 结果。我们预计,向研究社区公开提供代码的系统,当复杂轨迹的顺序被恢复时,将减少潜在的困惑,从而使恢复的轨迹具有进一步应用的价值,如速度估计。



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