# Evaluation and Recognition of Handwritten Chinese Characters Based on Similarities
## 3 Results and Discussion
### Comparison with Human Evaluations  
In order to realize the accurate evaluation of handwritten Chinese characters, researchers use various methods including feature similarity calculation, deep learning, rule-based and fuzzy, and matrix-based technical methods. A rule-based approach evaluates handwritten Chinese characters by formulating feature normative rules [48]. This method is easy to implement but relies on experts to formulate rules for each feature and requires re-formulation of new strokes. Furthermore, this method is only effective for standard Chinese character feature extraction, and the judgment of writing error type is fully determined by the rules, which limits the accuracy and diversity of evaluation. The method based on matrix fuzzy is to express the features that cannot be specifically described by handwritten Chinese characters by the membership degree of the fuzzy matrix [49]. It can solve the problem of vague concepts in handwritten Chinese characters, but the data acquisition relies on online devices and cannot obtain a detailed evaluation of handwritten Chinese characters. Aiming at the small data set of handwritten Chinese characters in banking communication business proposed in this paper, this paper uses a combined method based on feature similarity calculation for obtaining the detailed evaluation results of handwritten Chinese characters and a machine learning-based approach to learn the evaluation information of handwritten Chinese characters from input data. However, current machine learning methods also rely on traditional feature extraction methods [50]. We experimentally compared the computer evaluation results with the human evaluation results to demonstrate the accuracy of our proposed methods. We asked eight calligraphy experts to evaluate the handwritten Chinese characters. The experts rated the characters on a scale of one to five, based on how similar they were to the standard template characters. The human evaluation results are shown in the last row of Table 3.  
In Table 5, the first row shows the results evaluated by the correlation coefficient method. First, we averaged the values of similarity between the test characters written by each of the five subjects and the template ones. Then, the five similarity values were divided by the maximum one, and the result was multiplied by 100 to obtain the handwriting evaluation score. Furthermore, using the same process, we obtained the evaluation scores based on the pixel coincidence degree and cosine similarity values. For a reasonable evaluation, we arranged the evaluation results of the five subjects in \"A\", \"B\", \"C\", \"D\", and \"E\" in descending order. We see that the evaluation results of \"\(\overline{\left[\mathbb{A}\right]}\)\", \"\(\overline{\left[\mathbb{A}\right]}\)\", and \"\(\overline{\left[\mathbb{A}\right]}\)\" were consistent among the first six subjects and those of \"\(\overline{\mathbb{B}}\)\" were consistent among all the subjects. Finally, we averaged the scores of all the experts as the total human score. To quantitatively evaluate the ability disparity among the three methods in evaluating Chinese characters, the statistical distributions of errors in evaluation are illustrated using a box-and-whisker diagram shown in Figure 14. The diagram shows the variance between the scores converted from the similarity values and those given by the experts. We can find that both correlation coefficient and pixel coincidence degree had a maximum deviation of less than 20%, and the cosine similarity had a maximum deviation of greater than 50%. This means that the results of the former two methods in evaluating handwritten Chinese characters were closer to the human results than the third one.