# Evaluation and Recognition of Handwritten Chinese Characters Based on Similarities
## 4 Conclusions  
Previous studies have demonstrated the effectiveness of computer technology in evaluating calligraphy works from the perspective of art. However, these studies have not focused on the evaluation of the normative level of handwritten Chinese characters that are frequently used in electronic transactions in China. In this study, we proposed a strategy for evaluating the normative level of handwritten characters based on three different similarities, including correlation coefficient, pixel coincidence degree, and cosine similarity, to improve the accuracy for the recognition of ordinary handwritten Chinese characters.  
We found that the calculated similarities and related features are effective for evaluating the normative level of off-line handwritten characters and recognizing their content. The similarity-based methods facilitate the recognition of handwritten Chinese characters, while helping to standardize writing actions and thereby avoid scrawled and non-standard characters. In addition, the calculation process involved in our study was simple and the algorithm ran quickly and automatically. Most notably, the results from evaluation based on the correlation coefficient and pixel coincidence degree were close to those from human evaluation. Therefore, our proposed methods are practical in improving the recognition  
Figure 14: Statistical distributions of grading errors in similarity evaluation results.  
rate of ordinary handwritten characters and can be potentially used in various signature-related applications. However, our study still suffers from some limitations. Although the proposed methods are effective in recognizing relatively normative characters, they are insufficient for recognizing characters written in the running hand, which represents a high requirement for writers. For future works, we will explore methods that use cursive templates to evaluate running-hand characters and extend their applicability in evaluating various styles of handwritten Chinese characters.  
Conceptualization, Y.Z. and Z.Z.; methodology, X.Z., B.F. and Z.Z.; software, X.Z. and B.F.; validation, Y.Z., L.L. and Z.Z.; formal analysis, X.Z. and B.F.; data curation, X.Z. and Z.Z.; writing original draft preparation, X.Z. and B.F.; writing--review and editing, Z.Z., H.S. and Y.Z.; visualization, X.Z., B.F. and H.S.; supervision, Y.Z., L.L. and G.Z.; project administration, Y.Z.; funding acquisition, Y.Z. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61873307), the Hebei Natural Science Foundation (Grant Nos. F2020501040, F2021203070, F2022501031), the Fundamental Research Funds for the Central Universities under Grant N2123004, the Shenzhen Science and Technology Innovation Commission (SZSTI) (grant number: JCYJ201908081 81803703) and the Administration of Central Funds Guiding the Local Science and Technology Development (Grant No. 206Z1702G).  
Not applicable.  
The data presented in this study are publicly available by visiting: [[https://github.com/dlj0214/offline-handwritten](https://github.com/dlj0214/offline-handwritten)]([https://github.com/dlj0214/offline-handwritten](https://github.com/dlj0214/offline-handwritten)) (accessed on 15 August 2022).  
The authors declare no conflict of interest.