# Advanced Dense Text Detection in Graded Examinations Leveraging Chinese Character Components
## 3 Results
### Ablation Experiment  
To validate the performance improvement of the proposed model modifications on the overall architecture, ablation studies were conducted on our custom dataset. As shown in the Table 2, method 1 is the EAST base network (Basic), method 2 is the mini-component dataset through the feature pyramid as a multi-scale feature fusion (Mini-Component-Basic), method 3 is the addition of the attention mechanism of the ECA module (ECA-Basic), and method 4 is the CEE model in this paper. A comparative analysis of different approaches is presented in Table 2, demonstrating that the proposed model achieves significant accuracy improvement through the integration of the attention mechanism and multi-scale fusion strategy. According to Figure 7, the part feature fusion and ECA module each play a unique role in model performance enhancement, demonstrating significant improvements in overall performance and small target detection. In terms of Precision, the performance of the base model is 77.2%, which improves to 82.2% after fusing part features, an increase of 5.0%. This result indicates that fusing part features improves the recognition performance of complex Chinese character part shapes by enhancing the representation of local features. After adding the ECA module, Precision improves to 83.5%, indicating that the dynamic weighting mechanism can effectively highlight important part features and suppress the interference of background noise. When feature fusion is combined with the ECA module, the Precision of the final model reaches 84.6%, with a total improvement of 7.4%, verifying the significant contribution of the synergistic optimization effect of the two modules to the overall detection performance. \begin{table}
\begin{tabular}{c c c c c} \hline
**Structure** & **Model** & **Precision/\%** & **Recall/\%** & **F-Score/\%** \\ \hline Top-bottom & Basic & 78.6 & 77.1 & 77.8 \\ Structure & CEE & 85.7 & 84.2 & 84.8 \\ Wraparound & Basic & 77.8 & 75.4 & 76.7 \\ Structure & CEE & 84.3 & 82.5 & 83.4 \\ Left-right & Basic & 69.2 & 66.4 & 67.7 \\ Structure & CEE & 79.4 & 77.8 & 78.5 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance results of different Chinese character structure segmentation. \begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **Precision** & **Mini-Component mAP** & **Recall** & **F-Score** \\ \hline
1 & 77.2\% & 73.7\% & 75.4\% & 76.3\% \\
2 & 82.2\% & 75.8\% & 80.6\% & 77.7\% \\
3 & 83.5\% & 76.2\% & 81.2\% & 79.6\% \\
4 & 84.6\% & 80.7\% & 82.8\% & 83.6\% \\ \hline \end{tabular}
\end{table} 
Table 2: Ablation experiments results. In terms of small target detection, the advantages of the improved model are even more obvious. The small target mAP of the baseline model is only 69.2%, and the introduction of part features improves it to 74.3%, an increase of 5.1%. This reflects the fact that feature fusion improves the model's fitness on complex parts and narrow regions by enhancing the saliency of small target parts. The introduction of the ECA module improves the small target mAP to 73.6% and reaches 76.2% in the full model, for a total improvement of 8.0%. This reflects that the ECA module significantly improves the localization accuracy of the segmentation boundary by dynamically selecting key features, adapting to the complex and diverse characteristics of the boundaries of Chinese character components, and reflecting the fundamental role of the convolution module in constructing the boundary space representation. The comparative analysis in Figure 8a,b (unconstrained detection) reveals the CEE model's enhanced capability in detecting suboptimal-scale Chinese characters, particularly evident in the complete segmentation of the character \"\(\frac{\mu}{\mu}\)\" (star) (Row 1), where the basic model achieved only partial recognition. This validates the mini-component fusion strategy's effectiveness in resolving scale-dependent ambiguities.