# Image-Based Radical Identification in Chinese Characters
## 4 Discussion  
### Performance of the Proposed Model  
The model we proposed in this work successfully identifies the indexing radical (bushou) from hanzi images rendered with different font styles as long as it knows it. The experiments only adopted the 15 indexing radicals with the most entries in the dictionaries instead of the 214 bushou in use today to first validate the viability of classifying images of hanzi according to their indexing radical. From the general test, one notices that the reduced selection of radicals is unrealistic for real-life application, so we intend to expand the known elements for the identifier. The big challenge here is the unbalanced number of entries across the indexing radicals. If the list in Table 1 continues, one realizes that only 46 indexing radicals have more than 100 characters, and 162 radicals have at least 10 characters. Therefore, we need to create a valid data augmentation routine for a fair amount of training data for each indexing radical. Moreover, we intend to extend the work to tag the indexing radical block, which requires a more elaborate training dataset [31]. Both problems are more complex and should demand a more elaborate structure for the indexing radical identifier. During this work, however, we chose a simple CNN architecture with only two hidden layers because it solves the problem at hand. Considering the unbalanced number of entries for each indexing radical and how similar some of them are [32], this simple architecture will fail in more complex situations. For simpler applications, e.g., an auxiliary learning tool for a beginner student of Chinese as a foreign language or a content classification tool to check whether a print text is plant-related or not, a reduced number of known indexing radicals is enough, and so is a simpler identifier architecture. The proposed model can classify the images with different calligraphy styles, although the performance will still be better when the images share the characteristics of the training dataset. Comparing the performance of all three models for the three selected calligraphy groups in this work, one concludes that the identifier can deal with different calligraphy traits better than varying font sizes. Looking back at the training samples in Figure 5, it is clear that, although all of them are generated under equal window and font parameters, the actual character size varies, especially for the handwriting style. Therefore, if the regular-based model learns that the three-dot water occupies the left portion of a hanzi, from top to bottom, it might get confused in front of a three-dot water that occupies only half of the window height, even though the radical block remains at the left portion of the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Poem** & **Total of Hanzi** & **Known Radical Hanzi** \\ \hline A & 30 & 20 \\ B & 35 & 23 \\ C & 80 & 36 \\ D & 28 & 16 \\ E & 28 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of hanzi in the poems for the general test. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Poem** & **Only for Known Radical** & **For All Hanzi** \\ \hline A & 85.0\% & 68.0\% \\ B & 100.0\% & 79.3\% \\ C & 83.3\% & 51.7\% \\ D & 75.0\% & 54.5\% \\ E & 84.6\% & 53.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: F-score of the regular-based model on identifying the radicals in the poems. character. In comparison, the brush style has more variety regarding font size and style, leading to better robustness to font variation, as shown in Table 3.  
Besides the model validation with the likewise specially generated samples under well-controlled conditions, we wanted to check the identifier performance by introducing two extra variables in the general test with five ancient poems. First, we prepared the samples with a commonly found print font (PMingLiU) that was not in the training dataset.