# Evaluation and Recognition of Handwritten Chinese Characters Based on Similarities
## 1 Introduction
### Dataset Production  
For the recognition and classification of handwritten characters in different languages, various datasets have been created and used. Below we provide an overview of some of the datasets including IAM, RAMIS, SHIBR, Parzival, Washington, Saint Gall, Germana, Esposalles, and Rodrigo. The IAM dataset consists of 1539 pages of handwritten modern English text, written by 657 writers. An important feature of the dataset is that each of the three subsets is used for training, validation, and testing [20]. The RIMES database is composed of mail such as those sent by individuals to companies by fax or postal mail [21]. Moreover, 12,723 pages written by 1300 volunteers have been collected corresponding to 5605 mails. The SHIBR dataset is semi-annotated, easing the development of automated and semi-automated machine learning methods for document analysis applications [22]. The Parival dataset contains the epic poem Parzival, one of the most important epic works in the European Middle Ages, and is written with ink on parchment in the Middle High German language [23]. The [PERSON] database is a baseline database for text line segmentation, word spotting, and word recognition tasks. The Washington database consists of 20 historical handwritten document images written in English with longhand script and inktype pen in the eighteenth century [24]. RODRIGO is completely written in old Castilian (Spanish) by a single author and is comparable in size to standard databases. It is an 853-page bound volume divided into 307 chapters describing chronicks from Spanish history [25]. Most pages only contain a single text block of nearly calligraphed handwriting on well-separated lines. The Saint Gall database is made up of Latin manuscripts written in the Carolingian script [26]. GERMANA is the result of digitizing and annotating a 764-page Spanish manuscript from 1891, in which most pages only contain nearly calligraphed text written on ruled sheets of well-separated lines [27]. The Esposalles database is a Spanish historical handwriting document image database consisting of 173 document images [28]. The documents were written between 1451 and 1905, and they contain information from the marriage licenses of Spanish citizens. After comparison, we list the comparison of these datasets, as shown in Table 1.  
For the creation of the dataset, we invited five test subjects with large differences in writing level to write Chinese characters, with a total of 400 samples. At the same time, we use \" (means \"agree to proceed\") in italic font as the standard Chinese character template. In China, we are all beginners to other languages, and it is difficult to use other languages in practical applications. Regarding the banking or telecommunications business  
Figure 1: Image of Chinese characters “ (means “agree to proceed”) processing flowchart. are the most commonly used in teller services. Then the written characters were captured as digital images for subsequent comparisons. The (means \"agree to proceed\") in Song typeface were adopted as standard characters and captured as templates. Before image processing, eight senior hand-writing masters were selected to evaluate the written characters and the human evaluation results would be used later for comparison. Figure 2 shows the images of the characters after they were preprocessed by being grayed, binarized, denoised, and scaled. These preprocessing measures could eliminate the errors caused by the color difference of the background while minimizing the influence of writing position and character size. Character skeleton extraction [29; 30] is believed as a key preprocessing step and it can reflect the writing trace with less interference from contour noises. Hence, we extracted the skeleton of the characters before calculating the cosine similarity, as shown in Figure 3. The skeleton extraction process started with the binary images as presented in Figure 2, where the value of each pixel was 0 (black) or 1 (white). First, the binary Chinese character images were inverted.