# Sequence-Information Recognition Method Based on Integrated mDTW
## 4 Experiments
### Handwriting Character Recognition Systems  
We present the settings of the model hyperparameters to facilitate reproducibility. For the mDTW+Transformer model, we employ a 6-layer Transformer architecture to efficiently process temporal sequence data, leveraging the modified Dynamic Time Warping (mDTW) algorithm to enhance its capability to handle sequences with varying lengths and temporal distortions. Each layer of the Transformer is designed with a hidden state space dimension of 512. This configuration is crucial for balancing computational efficiency with the model's ability to capture complex temporal relationships. The choice of six layers is informed by preliminary experiments that indicate this depth provides a suitable trade-off between model complexity and performance on our targeted tasks without leading to significant overfitting or computational inefficiency. The hidden state dimension of 512 for each layer was selected based on its performance in capturing the intricacies of the data while maintaining a manageable model size and computational demand. These hyperparameters--specifically, the depth of the Transformer and the dimensionality of the hidden states--are essential components of our model's architecture that significantly influence its performance and efficiency in processing time-series data. The ultimate proposal of this paper is the HCCR experiment. We compare our method with state-of-the-art methods, including Benchmark, ICDAR-2011 Winner: VO-3, UWarwick, DropSample and DropSample-Ensemble-9 [26], NET4-subseq30 and NET123456 [27] and RNN-attention [19]. The illustrated results of those methods are recorded from the corresponding reference. In Table 2, \"top-1\", \"top-3\", and \"top-10\" refer to metrics for model prediction accuracy. \"Top-1\" accuracy means the proportion of times the model's first choice is the correct answer; \"top-3\" accuracy indicates the chance the correct answer is within the model's top three predictions; similarly, \"top-10\" accuracy refers to the correct answer being among the model's top ten predictions. As can be seen from Table 2, the result of mDTW+Transformer architecture is the current best performance among state-of-the-art methods. It should be mentioned that the results we utilize to compare are from all deep-learning-based end-to-end methods. Our method fails in the top-1 test against other state-of-the-art since we used the mDTW-cluster model to do the class task, which means that a test sample only refers to the nearest centers. Nevertheless, the top-3 result shows the achievements our method has made, indicating that the test samples find the correct centers as top-3 in 3755 classes. All these experiment outcomes indicate that our method has a huge potential in sequence processing tasks. We then combined it with the optimized Transformer to form the mDTW+Transformer architecture. As can be seen, this new architecture integrates the strengths of both components, surpassing the accuracy of all other compared SOTA methods. In recent times, there have been notable strides in the realm of sequence modeling, particularly with the advancements achieved by RNN-attention-based algorithms. These methodologies, leveraging attention mechanisms, have demonstrated significant progress across various domains. A noteworthy example is the RNN:Ensemble [19], which effectively reduced model scale while maintaining competitive performance. However, upon scrutinizing the data presented in Table 2, it becomes apparent that deep-learning-based models appear to have reached a performance plateau. The observed trend indicates that new methodologies have only marginally improved accuracy, with gains typically falling \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Simple** & **Normal** & **Complex** \\ \hline APM [22] & 34.2\% & 73.4\% & 82.7\% \\ CGE [23] & 87.1\% & 91.2\% & 85.3\% \\ LVMs [24] & 90.5\% & 92.0\% & 88.7\% \\ PMT [25] & 89.8\% & 93.5\% & 87.4\% \\ mDTW & 93.1\% & 94.2\% & 95.7\% \\ \hline \hline \end{tabular}
\end{table}