# Research on Bone Stick Text Recognition Method with Multi-Scale Feature Fusion
## 4 Experimental Analysis and Discussion
### Comparison of Different Fusions of Different Feature Layers with Each Other  
In order to verify the effectiveness of the fusion effect using five-layer features, this paper designed 10 groups of fusion methods and compared the experiments using the bottom-layer features alone for recognition. Various feature layers fused with each other, S1, S2, S3, S4, and S5, respectively, denote the first layer of the MS-ResNet features and the five layers of the last output layer of each residual from shallowest to deepest. The bottom layer features S5 and the middle layers are fused with each other. For example, the first-, third-, and fifth-layer features are fused as S1+S3+S5. The following experiments were conducted to fuse different feature layers on bone stick text recognition, and their various effects are shown in Table 1.  
As can be seen from the table, S5 represents the accuracy of recognition originally using only single-layer features, which is only 88.6%, and the classification results of fusing different underlying features are all higher than 88.6%. Multi-scale feature fusion  
Figure 8: (**a**) original image (**b**) brighter (**c**) contrast enhance (**d**) flip (**e**) rotating.  
Figure 7: Partial images of the data set.  
improves the accuracy of bone stick text recognition to different degrees for different layer fusion methods compared with single high-level feature classification, indicating that each intermediate feature layer contributes to bone stick text recognition. Therefore, the effectiveness of the fusion of different underlying features to enhance the fine-grained features of text and improve the accuracy of bone stick text recognition is verified, and the superiority of the multi-scale fusion approach of this paper's model is validated.  
In Figure 9 below, there are 23 samples for \"Man\" and 23 samples for \"Shuo\", which are similar in number and shape. The original ResNet is weak in extracting details, and \"Man\" is identified as \"Shuo\". There are 41 samples for \"fruit\" and 70 samples for \"public opinion\", which have a similar number of samples and a similar morphology. The original network model incorrectly identifies \"Guo\" as \"Yu\". The MS-ResNet model with multi-scale feature fusion allows the features to better capture text details and shallow contours and reduces the recognition of misspellings with similar morphological features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Recall/\(\%\)** & **Precision/\(\%\)** & **F1/\(\%\)** & **Acc/\(\%\)** \\ \hline S5 & 79.3 & 88.2 & 83.5 & 88.6 \\ S1+S5 & 80.9 & 88.7 & 84.6 & 89.1 \\ S2+S5 & 80.2 & 86.2 & 83.1 & 89.2 \\ S3+S5 & 81.6 & 88.1 & 84.7 & 89.6 \\ S4+S5 & 81.9 & 88.0 & 84.9 & 88.6 \\ S2+S3+S4 & 80.0 & 87.4 & 83.5 & 88.2 \\ S1+S3+S5 & 81.5 & 89.9 & 85.6 & 89.4 \\ S1+S2+S5 & 81.8 & 87.7 & 84.7 & 89.5 \\ S2+S3+S5 & 83.0 & 88.3 & 85.6 & 89.9 \\ S2+S3+S4+S5 & 82.9 & 88.0 & 84.8 & 89.8 \\ MS\_ResNet & 83.4 & 88.9 & 86.0 & 90.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification results of different features and their integration and comparison.  
Figure 9: Validation of the MS-ResNet model (**a**) ResNet incorrectly identifies “Man” as “Shuo”; (**b**) MS-ResNet correctly identifies “Man”; (**c**) the image of “Shuo” is shown, and its stroke morphological features are similar to those of “Man”; (**d**) ResNet incorrectly identifies “Guo” as “Yu”; (**e**) MS-ResNet correctly identifies “Guo”; (**f**) the image of “Yu” is shown, and its stroke morphological features are similar to those of “Guo”.