# Evaluation and Recognition of Handwritten Chinese Characters Based on Similarities
## 3 Results and Discussion
### Neural Network Test Results
These results suggest that the recognition accuracy was not high when identifying characters written by four subjects simultaneously. This may be because a big difference was present among non-normative characters written by different persons. For example, the B-level characters written by one subject were likely to be similar to C- or D-level characters written by other subjects. Finally, we asked four subjects to handwrite \"\([\overline{\bar{n}}]\)\", \"\(\overline{\bar{n}}\)\", \"\(\overline{\bar{n}}\)\", \"\(\overline{\bar{n}}\)\" and \"\(\overline{\bar{n}}\)\" (means \"agree to proceed\") in three different levels labeled with \"A\", \"B\", and \"C\", and again, each of the characters was written 20 times for each level. The number of each character in each level was also 80. The classification results of \"\([\overline{\bar{n}}]\)\", \"\(\overline{\bar{n}}\)\", \"\(\overline{\bar{n}}\)\", \"\(\overline{\bar{n}}\)\", and \"\(\overline{\bar{n}}\)\" are shown in Figure 12a-d, respectively. In Figure 12a, we see that 69 of the A-level characters \"\([\overline{\bar{n}}]\)\" were correctly identified as A-level, while the remaining 11 were mistakenly identified as B-level; in other words, the recognition accuracy of A-level \"\([\overline{\bar{n}}]\)\" was about 86.3%. Since the recognition accuracy rates of B- and C-level \"\([\overline{\bar{n}}]\)\" were 93.8% and 89.2%, respectively, the total accuracy of \"\([\overline{\bar{n}}]\)\" characters in all different levels was about 89.2%. As shown in Figure 12b-d, we can deduce that the total recognition accuracy rates of \"\(\overline{\bar{n}}\)\", \"\(\overline{\bar{n}}\)\" and \"\(\overline{\bar{n}}\)\" were 83.3%, 83.8%, and 81.7%, respectively. These recognition results were much superior to those of characters written in five different levels. To verify the effectiveness of our similarity-based method in differentiating the Chinese characters, we asked 20 persons to write \"\([\overline{\bar{n}}]\)\", \"\(\overline{\bar{n}}\)\", \"\(\overline{\bar{n}}\)\", and \"\(\overline{\bar{n}}\)\" (means \"agree to proceed\") and adopted the aforementioned similarities as features for further classification. As described in Section 3.4, eight features were extracted, including four cosine similarities calculated based on non-skeleton images in four different ways, two cosine similarities calculated based on the skeleton images shown in Figure 9a, c correlation coefficient, and pixel coincidence degree. The results of distinguishing each character from the other three ones are shown in Figure 13a-d. The recognition accuracy rates of the characters \"\([\overline{\bar{n}}]\)\", \"\(\overline{\bar{n}}\)\", and \"\(\overline{\bar{n}}\)\", were all 100%, and that of \"\(\overline{\bar{n}}\)\" is 98%. This proves that our similarity-based method worked well in distinguishing one character from the others [47]. To compare the performance of different machine learning algorithms in classifying the four characters, we also applied SVM, K-NN, and CNN to replace the BP neural network. The experimental results of using these methods to perform classification on the same features are shown in Table 4. From the table, we can find that the neural networks (BP and  
Figure 11: The results of a neural network test in which four subjects wrote the Chinese character in five different levels.