# Evaluation and Recognition of Handwritten Chinese Characters Based on Similarities
## 1 Introduction
Convolutional neural networks trained with MNIST and USPS and tested on ARDIS provided the highest accuracy rates of 58.80% and 35.44%, respectively. The NIST dataset represents a logical extension of the classification task in the MNIST dataset, and [PERSON] et al. [8] proposed a method to convert this dataset into a format that is directly compatible with classifiers built to process the MNIST dataset. [PERSON] et al. [9] provide a large and complex Arabic handwritten character dataset (HMBD) for designing an Arabic handwritten character recognition (AHCR) system. Using HMBD and two other datasets: CMATER and AIA9k, 16 experiments were applied to the system. By using data augmentation, the best results for test accuracy of the two datasets were 100% and 99.0%, respectively. In order to recognize handwritten digit strings in images of Swedish historical documents using a deep learning framework, a large dataset of historical handwritten digits named DIDA is introduced, which consists of historical Swedish handwritten documents written between 1800 and 1940 [10]. [PERSON] et al. [11] proposed an image-only model for performing handwritten Chinese character recognition. The motivation for this approach came from the desire to have a purely data-driven approach, thus avoiding any associated subjective decisions that require feature extraction. However, this method requires the complexity of computing fuzzy similarity relations. The study showed that frame and single structural features proved to be the easiest to classify, with a 100% classification rate. The upper and lower and surrounded characters are more challenging, with 85% and 83.33% accuracy, respectively. In order to realize the accurate evaluation of handwritten Chinese characters, researchers continue to use various methods. [PERSON] et al. [12] first proposed a concept learning-based method for handwritten Chinese character recognition. Different from the existing character recognition methods based on image representation, this method uses prior knowledge to build a meta-stroke library, and then uses the Chinese character stroke extraction method and Bayesian program learning to propose a Chinese character conceptual model based on stroke relationship learning. [PERSON] et al. [13] proposed an online handwritten Chinese character end-to-end recognizer based on a new recurrent neural network (RNN). In the system, the RNN was used to directly process the raw sequence data through simple coordinate normalization, and the recognition accuracy reached 97.6%. In order to solve the problems of low-quality historical Chinese character images and lack of annotated training samples, [PERSON] et al. [14] proposed a transfer learning method based on a generative adversarial network (GAN) to alleviate these problems. After the fine-tuning training process with the real target domain training data set, the final recognition accuracy of historical Chinese characters reached 84.95%. [PERSON] et al. [15] proposed a method combining deep convolutional neural networks and support vector machines. The features of Chinese characters were automatically learned and extracted by using a deep convolutional neural network, and then the extracted features were classified and recognized by a support vector machine. Experiments showed that the deep convolutional neural network can effectively extract features, avoiding the shortage of manual feature extraction, and the accuracy was further improved. To achieve accurate evaluations, it is necessary to introduce more features and calculate various indexes which can describe the global or local features of characters. [PERSON] et al. proposed 22 global shape features and a new 10-dimensional feature vector to describe the overall properties of characters and represent their layout information, respectively [16]. Based on these features, they developed an artificial neural network model to quantitatively evaluate handwritten Chinese characters and its performance was comparable to that of human beings. [PERSON] et al.