# Writing Order Recovery in Complex and Long Static Handwriting

arXiv: https://arxiv.org/abs/2406.03194 · HTML: https://mlsanity.com/paper/2406.03194/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 77 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health](https://mlsanity.com/paper/2202.12760/) (2022)
- [A Perspective Analysis of Handwritten Signature Technology](https://mlsanity.com/paper/2405.13555/) (2024)
- [Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders](https://mlsanity.com/paper/1906.06142/) (2019)
- [Online trajectory recovery from offline handwritten Japanese kanji characters](https://mlsanity.com/paper/2009.04284/) (2020)
- [Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network](https://mlsanity.com/paper/1801.07211/) (2018)
- [iDeLog: Iterative Dual Spatial and Kinematic Extraction of Sigma-Lognormal Parameters](https://mlsanity.com/paper/2401.15473/) (2024)
- [Temporal evolution in synthetic handwriting](https://mlsanity.com/paper/2401.15472/) (2024)
- [Dynamically enhanced static handwriting representation for Parkinson's disease detection](https://mlsanity.com/paper/2405.13438/) (2024)
- [Field typing for improved recognition on heterogeneous handwritten forms](https://mlsanity.com/paper/1909.10120/) (2019)

## Built on by (most cited first, 3 of 3)

- [Layout Stroke Imitation: A Layout Guided Handwriting Stroke Generation for Style Imitation with Diffusion Model](https://mlsanity.com/paper/2509.15678/) (2025)
- [Handwriting Trajectory Recovery with Diffusion Models](https://mlsanity.com/paper/2607.03422/) (2026)
- [Draw This First](https://mlsanity.com/paper/2608.12064/) (2026)

## Abstract (arXiv)

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.