# Evaluation and Recognition of Handwritten Chinese Characters Based on Similarities
## 1 Introduction  
The field of text recognition can be roughly divided into two categories. The first category is for text content identification, and the second category is for authorization identification of signatures. The method proposed in our paper contains both functions of identifying the content and authorization. Therefore, this research lies between the need for confirmation of information content and the need for authorization identification, and this field has not been studied by others. After continuous exploration in recent years, researchers have found many effective Chinese character recognition methods, and the recognition accuracy has also been greatly improved. However, a major factor restricting the further improvement of the accuracy of Chinese character recognition at present is that there are some very scribbled Chinese characters in the test set. There must be differences in the Chinese characters written on the touch screen, especially when conducting banking transactions. When writing the words \"[2][][]\" (means \"agree to proceed\") on a touch screen, it will be different from the Chinese characters we normally write on paper. Sometimes, handwritten characters can be difficult for manual recognition. If the neatness of the writing of Chinese characters can be standardized in advance, it will be of great benefit to improve the recognition rate. The recognition of off-line handwritten Chinese characters has received considerable research attention due to its wide application and ability to offer significant economic and social benefits [1]. The recognition is usually realized based on the features derived from static two-dimensional (2D) images [2]. However, major challenges still remain due to thelarge number of Chinese characters in various writing styles, similar and easily confused characters, and the lack of handwritten Chinese character sets [3; 4]. Despite that, many methods have been developed to improve the recognition accuracy, even the best model, which is based on the traditional modified quadratic discriminant function (MQDF), falls far behind human beings when it comes to recognition accuracy [5]. One of the reasons may be that the algorithms have little experience in analyzing scrawled and non-standard handwritten Chinese characters. Therefore, it is important to develop a quantitative measure for evaluating handwritten Chinese characters based on their normative level. Such a measure not only helps to standardize writing actions and improve the recognition accuracy but also facilitates the development of Chinese character identification methods based on handwritings. Up to now, most Chinese character evaluation methods are focused on calligraphy works with the aim to help learners improve their writing skills. According to the characteristics of characters in different fields, researchers have created various datasets to conduct in-depth research on handwritten Chinese character recognition. For example, [PERSON] et al. used computer technology to simulate human's understanding of calligraphy and built a database that contained a large number of images of handwritten characters which were labeled as \"beautiful\" or \"ugly\" by two calligraphy connoisseurs [6]. By training a classifier based on the K-nearest neighbors algorithm, they labeled the test images automatically without quantitative details. [PERSON] et al. [7] introduced a new image-based dataset of handwritten historical digits named Arkiv Digital Sweden (ARDIS). The images in the ARDIS dataset are drawn from 15,000 Swedish church records written in different handwriting styles by different priests in the 19 th and 20 th centuries. The constructed dataset consists of three one-digit datasets and one-digit string datasets. Experimental results show that machine learning algorithms (including deep learning methods) face difficulties in training on existing datasets and testing on ARDIS datasets, resulting in low recognition accuracy.