# Sequence-Information Recognition Method Based on Integrated mDTW
## 4 Experiments
### Non-Rigid Sparse Point Matching  
It is important to introduce the data we utilized. CASIA-HWDB, with its high-resolution color images and well-defined binary representation, stands as a valuable resource for offline handwritten character recognition research. Point matching constitutes a classical and intricate challenge within the expansive domain of image registration research. When juxtaposed with the task of matching feature points in images, the endeavor of aligning points within handwritten characters introduces three distinctive characteristics that significantly shape the complexity of the problem. There is the imposition of a nonlinear spatial transformation when attempting to match points in handwriting characters. This nonlinear transformation induces a notable disturbance in the distribution of the two sets of matching points, introducing intricacies that are not present in linear transformations. This spatial nonlinearity poses a formidable obstacle, demanding sophisticated techniques to navigate and effectively align the points within the given framework. Especially as depicted in Figure 5, the significance of the point-matching process becomes strikingly evident when confronted with the notably distinct writing styles of various authors for the same character. This figure illustrates a scenario where two different individuals have written the same character, yet their unique writing styles result in significantly different representations. The red and blue line segments in the figure represent two online handwritten datasets, which are handwritten text samples that include time information. These datasets capture the intricate details of each author's writing style, encompassing the speed, pressure, and direction of the pen strokes. The green line segments indicate the point alignment relationships calculated using the modified Dynamic Time Warping (mDTW) algorithm. This algorithm allows us to match corresponding points between the two datasets, even when they exhibit different lengths and writing styles. Upon examining the point alignment process, it is evident that our results are highly accurate, as the green line segments closely adhere to the contours of the red and blue line segments, indicating a remarkable degree of similarity between the matched points. This underscores the effectiveness of our approach in addressing the challenges posed by diverse writing styles and emphasizes the crucial role of accurate point matching in online handwritten text recognition. The nature of handwriting introduces an idiosyncratic challenge: individuals often connect different strokes for the sake of convenience during the writing process. This tendency results in a considerable number of errors in the sets of matching points. The interconnection of strokes complicates the matching task, requiring an intelligent and adaptable approach to correctly align points despite these inherent irregularities. The feature points of Chinese characters exhibit a geometric dispersion that adds an additional layer of difficulty to the matching process. Unlike more regularly structured shapes, the geometric dispersion of feature points in Chinese characters makes it challenging to rely on local spatial distribution similarity for effective point matching. Consequently, conventional methods that hinge on local spatial characteristics may fall short when confronted with the intricacies of matching points within these dispersed geometric patterns. Point matching in handwriting characters is distinguished by the introduction of nonlinear spatial transformations, the prevalence of stroke connections leading to errors, and the geometric dispersion of feature points in Chinese characters. Addressing these challenges requires the development and application of advanced methodologies that can adeptly handle the intricacies inherent in the matching process within this unique context.