# Image-Based Radical Identification in Chinese Characters
## 3 Results
### Model Validation for the 15 Indexing Radicals  
The identifiers were evaluated first for the validation samples that share the same font style as the training dataset. The confusion matrices with the validation results by all three models are presented in Figure 7. The values in the main diagonal show the percentage of true positive (_TP_) detections per class, i.e., how many of the predictions of a given class belong to that class. Besides the true positive detections, the false positive (_FP_) and the false negative (_FN_) predictions were also relevant because both compute the number of misclassifications. Given _TP_, _FP_, and _FN_, the overall performance of the classifier model was evaluated in terms of the F-score,  
\[\text{F-score}=\frac{1}{\frac{1}{\textit{Precision}}+\frac{1}{\textit{Recall}}} \tag{1}\]  
where \(\textit{Precision}=\textit{TP}/(\textit{TP}+\textit{FP})\) and \(\textit{Recall}=\textit{TP}/(\textit{TP}+\textit{FN})\)[30]. The regular-based and brush-based models could identify the radicals with average F-scores of \(\sim\)98.6% and \(\sim\)96.8%, respectively, while the handwriting-based model yielded an average F-score of \(\sim\)91.8%. These values are consistent since the identifiers know both the radicals and the font style inside the validation set. Comparing the confusion matrices in Figure 7, one concludes that the regular-based and brush-based models presented the best performance for the validation samples of the same font style as the one found in the training dataset. This behavior comes from the nature of the font style in each batch. Despite the particularities among different fonts, the regular category is upright and boxy, with clean strokes. The brush calligraphy, in turn, has an artistic flavor. Although some brush fonts are rigid with clean strokes, others are fluid and tend to join adjacent strokes, comparable to western curse calligraphy. The handwriting fonts have similar behavior, aside from which the HuaKang handwriting has diverse scale factors within identical font size properties, leading to the (_TP_) percentage drop compared to the other two models. The wrong detection percentages are not explicit in Figure 7, but one concludes from the scale bar that the models did not concentrate the (_FP_) and (_FN_) detections in any particular class, i.e., they did not misattribute the main visual features of an indexing radical to another. Indeed, a closer look showed that the false detections are evenly distributed outside the main diagonal, with values below 5%, and they are more noticeable in the handwriting model due to the calligraphy style and the inner scaling properties of the font itself. Even though the wrong classifications did not concentrate on a particular pair of actual-predicted classes, the classes \"grass\", \"wood\", \"hand\", and \"fire\" were the most common outputs of the _FP_ predictions. On the other hand, \"fire\", \"mouth\", and \"earth\" are the ones with more _FN_ predictions. Both _FP_ and _FN_ misclassifications are reasonable by analyzing the sub-lexical elements of the hanzi. For example, a significant number of characters have the block \"wood\" (\(\
warrow\)) as bujian, although the indexing radical is another (to mention a few, e.g., [100]). Another contributing factor for the higher _FP_ and _FN_ rates in the handwriting-based identifier is the individual styles the handwriting fonts simulate, creating sub-variations for the default ones of the indexing radical block. The model was then validated for a dataset with a font style other than the one in the training dataset to evaluate its robustness to different calligraphy styles. For instance, the regular-based model was validated with the brush and handwriting datasets, and so forth.