# Sequence-Information Recognition Method Based on Integrated mDTW
## 4 Experiments
### Non-Rigid Sparse Point Matching
While point-matching is not the central focus of our investigation, its significance lies in its capacity to serve as a reflective indicator of the performance of mDTW. An extensive exploration of point-matching experiments has been conducted to comprehensively assess the prowess of mDTW. The findings reveal that mDTW takes into account various aspects such as handwriting trajectory, direction distribution, moving displacement, and stroke connection in tandem to determine the outcomes of the matching process. A detailed analysis of the experimental results, as depicted in the figures, allows for an intuitive description of the prevailing scenario wherein correctly matched points dominate themajor portions of the matching results. The proposed mDTW algorithm has effectively met our stipulated requirements, demonstrating a reliable point-matching performance essential for subsequent clustering endeavors. The amalgamation of trajectory, distribution, displacement, and stroke considerations in the mDTW framework contributes to its efficacy in delivering accurate and meaningful matching outcomes, therefore substantiating its suitability for the specified clustering objectives. To give a further analysis and evaluation on mDTW, we compare our method with two traditional Chinese handwriting points matching methods [22; 23]. These two algorithms are image-based. In other words, they do not consider temporal information while matching. Similar to some other Chinese handwriting research, in addition, we also compared two of the latest point-matching methods, LVMs [24] and PMT [25]. LVMs, or Large Vision Models, leverage semantic features derived from large-scale vision models to enhance geometry-based shape feature learning, making them highly effective for non-rigid point cloud matching. PMT, or Proxy Match Transform, introduces a high-order feature transform layer to efficiently match geometric shapes in 3D point clouds, particularly excelling in assembling geometric shapes by establishing reliable local correspondences between part surfaces. We divide the sequence samples into three groups by their complexities, which are simple (0-10 points), normal (20-50 points) and complex (more than 50) points. The summary statistics for comparison are illustrated in Table 1. APM refers to Asymmetric Point Matching, while CGE stands for Constrained Global Energy function. We randomly selected 200 pairs of Chinese handwriting samples from the OLHWDB1.1 dataset. In this dataset, the shortest sample contains 3 points, while the longest one has 216 points. OLHWDB (Online Handwritten Chinese Character Database) is the online handwritten version of HWDB (Handwritten Chinese Character Database), capturing handwritten point sequences with both spatial and temporal information. In contrast, HWDB represents characters in image format and lacks sequential data. The OLHWDB1.1 dataset comprises 3755 distinct Chinese characters, each represented by numerous handwriting samples. For our point registration experiment, we randomly selected 200 of these characters to ensure a diverse and representative subset for analysis. Since the purpose of this experiment is to demonstrate the accuracy of point registration, it is not necessary to select all Chinese characters. The experiment data are labeled and checked manually. Results reported in Table 2 indicate that the proposed mDTW has an advantage against state-of-the-art methods in all complexity conditions. A possible explanation for this is that mDTW takes temporal information as a significant factor. Since online handwriting trajectories may have severe spatial noises in different samples, the writing order for every stroke should be the same. Thus, sequence-based mDTW performs much better than image-based methods in such kinds of tasks. Figure 5: Point-matching results, black line indicates wrong, green indicates right. Red and blue lines are two handwriting characters.