# Image-Based Radical Identification in Chinese Characters
## 5 Conclusions  
A CNN-based hanzi indexing radical identifier is demonstrated in this work for 15 indexing radicals and 3 font style families, namely regular, brush and handwriting. The identifiers were trained individually for each style and validated with all three calligraphies, achieving an average F-score of \(\sim\)95.7% in closed tests when the testing font matches the one in the training session. The performance dropped to different degrees for all identifiers when the training and testing fonts were different due to various factors such as size, thickness, and style variation. For instance, the regular-based identifier classified the brush style and handwriting samples with an average F-score of \(\sim\)83.0% and \(\sim\)41.8%, respectively. The latter is poorer because the handwriting calligraphy has a sharper size and stroke variation than the brush style, which is still closer to the regular font despite the cursive nature of some brush calligraphies. In this sense, the brush-based identifier showed better performance in different font styles, with an average F-score of \(\sim\)70% for both regular and handwriting samples since it holds both the upright boxy and the flourished propertiesof these styles. Lastly, the handwriting-based identifier showed the poorest performance for the regular (\(\sim\)30.9%) and brush samples (\(\sim\)60.4%), probably due to the overall size variation of the training samples compared to the test set. Aside from the test for different font styles, an open, general test was designed to evaluate the identification robustness to factors such as font, offset, and blurring variables. In this scenario, the identifier showed variable F-scores because its performance depends mainly on how many indexing radicals the model knows. Therefore, it is essential to update the training dataset to encompass more classes, as all 214 indexing radicals have characters of daily use. Moreover, combining all three font style categories in one training dataset should increase the identifier robustness as well.  
Conceptualization, Y.T.W.; methodology, Y.T.W.; software, Y.T.W.; validation, Y.T.W.; formal analysis, Y.T.W.; investigation, Y.T.W.; resources, Y.T.W.; data curation, Y.T.W.; writing--original draft preparation, Y.T.W.; writing--review and editing, E.F. and C.K.S.; visualization, Y.T.W.; supervision, E.F.; project administration, E.F.; funding acquisition, E.F. and C.K.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CNPq, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico--Brazil under the grant number 142580/2018-0.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
CJK Chinese-Japanese-Korean  
CNN Convolutional neural networks  
FN False negative  
FP False positive  
OCR Optical character recognition  
TP True positive