# Sequence-Information Recognition Method Based on Integrated mDTW
## 2 Related Works
### Related Works of Sequence Data  
Time-series classification and regression are important techniques for analyzing and modeling data that changes over time. These methods have wide-ranging applications, including Human Activity Recognition, Handwritten Chinese Character Recognition, Earth Observation, Medical Diagnosis, and more. In this study, we will focus on the application of time-series classification and regression in Human Activity Recognition and Handwritten Chinese Character Recognition and provide an overview of the latest developments and challenges in these fields.  
Human activity recognition (HAR) is the identification or monitoring of human activity through the analysis of data collected by sensors or other instruments [14]. The recent growth of wearable technologies and the Internet of Things has resulted not only in the collection of large volumes of activity data [15] but also in the easy deployment of applications utilizing these data to improve the safety and quality of human life. HAR is, therefore, an important field of research with applications including healthcare, fitness monitoring, smart homes [16], and assisted living [17].  
Handwritten Chinese Character Recognition (HCCR) is a field of study focused on the automatic recognition of handwritten Chinese characters. This technology plays a crucial role in various applications, such as document digitization, optical character recognition (OCR) systems for forms, checks, and IDs, and handwriting input devices. Since the 1980s, HCCR has been a significant research area in pattern recognition due to the complexity and wide variety of Chinese characters. For instance, in [PERSON] et al.'s paper [18], discriminative feature-extraction methods and discriminative learning quadratic discriminant function classifiers were employed, achieving outstanding recognition rates on challenging online and offline HCCR datasets CASIA-OLHWDB and CASIA-HWDB. The best online recognition rates for single characters were 95.28% (DB1.0, 4037 characters), 94.85% (DB1.1, 3926 characters), and 95.31% (ICDAR 2013 Competition DB, 3755 characters). The best offline recognition rates were 94.20% (DB1.0), 92.08% (DB1.1), and 92.72% (ICDAR 2013 Competition DB). More recently, [PERSON] et al. [19]. utilized an RNN, combining Bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which processes raw sequential data directly without converting online handwriting trajectories into image-like representations, achieving an end-to-end recognition rate of 98.15% on the ICDAR-2013 dataset.  
Compared to other related works, it has been challenging to achieve better performance through deep-learning training. Collectively, there seems to be some evidence to indicate that existing Deep-learning-based HCCR methods have reached the performance bottleneck. [PERSON] et al. [20] achieved groundbreaking results in the field of HCCR by integrating traditional direction-decomposed feature maps with deep convolutional neural networks; however, it is important to note that these achievements, while groundbreaking, are now seven years old. Additionally, [PERSON] et al. [21] introduced a novel approach in the HCCR domain leveraging a Siamese neural network to predict the similarity between handwritten Chinese characters and template images, showing promising generalization to new classes not seen in training, but the method may struggle with limited data scenarios and the complexity of optimizing Siamese networks. However, by integrating mDTW and Transformer, a significant performance breakthrough was successfully achieved with this framework, demonstrating its potential in other specific sequence-data-related domains.