# Evaluation and Recognition of Handwritten Chinese Characters Based on Similarities
## 3 Results and Discussion
### Neural Network Test Results  
In this section, we used a neural network [39, 40, 41, 42, 43, 44] to evaluate the effect of previously calculated similarity values on the evaluation of handwritten Chinese characters. First, we asked one subject to handwrite four Chinese characters \"[\(\overline{\mathbb{A}}\)\", \"\(\overline{\mathbb{B}}\)\", \"\(\overline{\mathbb{A}}\)\", \"\(\overline{\mathbb{A}}\)\", and \"\(\overline{\mathbb{A}}\)\" (means \"agree to proceed\") with five different normative levels labeled with \"A\", \"B\", \"C\", \"D\", and \"E\". Each of the Chinese characters was written 20 times for each degree and thus \(100\)\"[\(\overline{\mathbb{A}}\)\", \(100\)\"\(\overline{\mathbb{A}}\)\", \(100\)\"\(\overline{\mathbb{A}}\)\", \(100\)\"\(\overline{\mathbb{A}}\)\"]\", and \(100\)\"\(\overline{\mathbb{A}}\)\" were obtained. Then, we calculated the correlation coefficient, coincidence degree, and cosine similarity degree between each 100 handwritten Chinese characters and their templates using the previously described methods [45]. It is worth noting that six different results were obtained from the cosine similarity calculation; plus the correlation coefficient and coincidence degree, there were a total of eight features for classifying the normative level of the Chinese characters [46]. Here, we used a neural network to classify each 100 handwritten characters and the results are shown in Figure 10.  
Figure 10a-d shows the classification results of the four handwritten Chinese characters. Specifically, \"A\", \"B\", \"C\", \"D\", and \"E\" represent the normative level; the numbers and percentages in the green grids are the numbers and percentages of characters correctly classified as the corresponding levels, respectively. In the gray grids, two percentages were used to represent the total recognition accuracy rate (in green) and error rate (in red). The total recognition accuracy rates of \"[\(\overline{\mathbb{A}}\)\", \"\(\overline{\mathbb{B}}\)\", \"\(\overline{\mathbb{A}}\)\", \"\(\overline{\mathbb{A}}\)\" and \"\(\overline{\mathbb{A}}\)\" (means \"agree to proceed\") were 94%, 98%, 93%, and 94%, respectively. These results verified that our similarity-based method worked well in distinguishing the normative levels of characters written by one person. To distinguish the normative levels of characters written by different persons, we asked another four subjects to handwrite \"[\(\overline{\mathbb{A}}\)\", \"\(\overline{\mathbb{B}}\)\", \"\(\overline{\mathbb{A}}\)\"]\" and \"\(\overline{\mathbb{A}}\)\" in five different normative levels with \"A\", \"B\", \"C\", \"D\", and \"E\", and again, each of the characters was written  
Figure 10: Results of a neural network test in which one subject wrote Chinese characters (**a**) \"[\(\overline{\mathbb{A}}\)\" (**b**) \"[\(\overline{\mathbb{A}}\)\" (**c**) \"\(\overline{\mathbb{A}}\)\" (**d**) \"\(\overline{\mathbb{A}}\)\" (means \"agree to proceed\") in five different normative levels. 20 times for each degree. Then, we put all the characters in the same normative level written by four persons together for further classification. This Chinese character set contained \(80^{\prime\prime}[\overline{\bar{n}}]^{\prime\prime}\), \(80^{\prime\prime}\overline{\bar{n}}^{\prime\prime}\), \(80^{\prime\prime}\overline{\bar{n}}^{\prime\prime}\), and \(80^{\prime\prime}\overline{\bar{n}}^{\prime\prime}\) in each level. The identification results of the character \"\(\overline{\bar{n}}\)\" are shown in Figure 11. Among the 80 A-level characters, 79 were correctly identified as A-level, while the remaining 1 character was mistakenly identified as B-level. Among the 80 B-level characters, 53 were correctly identified as B-level, and 2 characters were mistakenly identified as A-level, 11 as C-level, 4 as D-level, and 10 as E-level. The total recognition accuracy rate of B-level characters was only 66.3%. The recognition accuracy rates of C-, D-, and E-level characters were also less than optimal and mostly not greater than 60%.