# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 3 Handwritten Chinese Character Recognition
### Deep Learning Based Recognition Methods
#### 3.2.1 Improve the Accuracy of Recognition
[44] proposed a central loss-based metric learning character recognition algorithm in 2017, which combines metric learning and the ResNet network to achieve 97.03% accuracy on the ICDAR 2013 dataset. In addition, using a modified ResNet network to identify HCC is [PERSON] et al. [45], who proposed a discriminant weighting method for cross-entropy loss calculation to handle recognition errors in the training phase in 2019. The method achieves the highest known accuracy of 98.79% on the ICDAR 2013 dataset when using a sparse training technique and the specific condition of utilizing the testing mini-batch mean and variance for batch normalization. [PERSON] et al. [46] combined triplet loss and softmax with cross-entropy loss as the loss function of the CNN model for local discriminant training of HCCR. Furthermore, they used the Conditional Random Field (CRF) to achieve global optimization. In addition, [PERSON] et al. [47] proposed two new loss functions to accomplish the HCCR task in 2019. They created the character template to address the inherent similarity between Chinese characters. Depending on the complexity of the classification, instance loss can reduce category differences while severely penalizing outlier instances of handwritten Chinese characters. Other scholars have worked on improving the accuracy of HCCR in terms of the specific structure of Chinese characters, the handwriting styles of different people, and the expansion of the dataset. In 2017, [PERSON] et al. [48] added local features of Chinese characters to the traditional CNN model. This multi-supervised training method learns both global and regional features of HCC and achieves a then state-of-the-art accuracy of 97.42% without data augmentation and model ensemble. It also shows the potential of combining structural information of Chinese characters for recognition. [PERSON] et al. [49] also considered the local features of Chinese characters and proposed a convolutional neural network combining multiple attention mechanisms, MCANet. It maps the last convolutional feature into multiple attention graphs, using contrast loss to restrict different attention mechanisms to focus on different subfeature spaces, eventually improving the accuracy to 97.66%. [PERSON] et al. [50] proposed an adaptive feature learning (AFL) algorithm to solve the difference of writing styles of different authors. The model improves the accuracy of the model by combining the prior knowledge of printed data and the author independent semantic feature information. [PERSON] et al. [51] proposed a writing style confrontation network (WSAN) to weaken the impact of recognition accuracy caused by different writing styles. The network mainly consists of two classifiers: character classifier and writer classifier. By applying joint optimization to the top of the two classifiers, the loss value of the writer classifier is maximized, and the loss value of the character classifier is minimized, thus reducing the impact of writing style in handwritten Chinese character recognition. [PERSON] et al. [52] improved the recognition performance of the CNN model by extending the dataset through random elastic deformation, shear transformation, and rotation in a small range. [PERSON] et al. [53] segmented the character images into sub-images based on the separability of the Chinese character structure. They increased the number of training data by recombining the sub-images. By using this training dataset that includes reconstructed and rotated characters, extracting features using DRN, and applying the center loss function, the final recognition accuracy of this method reaches 97.53% in the ICDAR 2013.