# Image-Based Radical Identification in Chinese Characters
## 3 Results
### Performance in General Test  
In the general test, the regular-based model processed the poems A-E, rendered in the PMingLiU font from the MingLiU family (Figure 6). The font choice allows one to evaluate the proposed indexing radical identifier for a popular and unknown font style, even though the previous validation step already analyzed the three models for different calligraphy groups from the one in the training dataset. Another variable comes from the image processing operations to extract the individual characters of the poems, which add noise and shift to the input data. Moreover, since the PMingLiU font is regular and square, the test was run with the regular-based identifier alone. The results for poem A (\"The Quatrain of Seven Steps\" by [PERSON]) are shown in Figure 8. Among the 30 hanzi in the poem, 20 are from a radical among the 15 labeled classes. The correct classifications of these 20 characters are in blue, and the misclassifications are in red. As observed, the model predicted correctly 17 out of the 20 known cases, representing an F-score of 85.0%. This behavior agrees with the values obtained in the validation stage, though there was a drop in the performance. This loss is probably due to the hanzi extraction routine. While the validation samples in the previous step are individually generated with the same parameters of the training dataset, the hanzi in the poems are extracted as contents of a bigger image, considering that any character will be inside a fixed-size square. Therefore, they are bound to suffer from offset, resolution, and cropping to fit them into the expected input size of the indexing radical identifier. Aside from the characters with known indexing radicals, there are still ten hanzi in the poem whose indexing radicals are unknown to the classifier (in black). The actual class label of these characters is indicated with a question mark in Figure 8. Since the model does not know these indexing radicals, the prediction would not match the actual class, and the F-score dropped to 68.0%. It is nevertheless interesting to analyze the results of this scenario with some case studies, as shown in Figure 9.  
The leftmost column shows the image input as the model sees it, and the second column indicates the actual radical (highlighted in red). The examples in Figure 9a,b coincide with the base form of their radical, \"bean\" and \"center\", respectively; therefore, the whole hanzi is marked in red, and the examples in Figure 9c,d are a combination of their radical, \"day\" and \"eye\" respectively, with other strokes. Since none of these  
Figure 8: Classification results of the regular-based model over poem A with the correct predictions (blue), the misclassifications (red), and the unknown radicals (black). The numbers above the hanzi represent “predicted class (actual class)”. The actual class is indicated with a question mark when it is unknown to the model. four radicals are in the training set, the model could not identify them correctly. Instead, the identification was based on other morphological traits of the character, as indicated in red in the third column. First, both hanzi in Figure 9a,b were pointed to the radical \"mouth\" because they both have a square, which matches the base form of \"mouth\" as indicated in the last column of Figure 9 in blue. Likewise, the example in Figure 9c was classified based on the second form of the radical \"fire\" (shown in blue). Lastly, the hanzi in Figure 9d was misclassified as the radical \"wood\" due to its composition. The block on the left side is \"wood\", while the block on the right side is \"eye\". Since the model does not recognize the radical \"eye\", it concluded naturally that the character belonged to the radical \"wood\" instead. Finally, the same analysis is repeated for poems B-E. Table 4 summarizes the total of characters and the number of known radical hanzi in the poems.