# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 3 Handwritten Chinese Character Recognition
### Novel Methods of Applying Other Knowledge
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Year** & **Experimental Platform** & **Framework** & **Accuracy (\%)** & **Storage (MB)** \\ \hline \multirow{2}{*}{STN-Residual-34 [37]} & \multirow{2}{*}{2016} & Intel(R) Xeon(R) E5-2680v3 & \multirow{2}{*}{Torch7} & \multirow{2}{*}{97.37} & \multirow{2}{*}{92.3} \\  & & 2.50 GHz CPU, one NVIDIA Titan X GPU & & & \\ \hline ResNet + center loss [44] & 2017 & GTX TITAN X GPU & Caffe & 97.03 & n/a \\ \hline M-RBC + IR [35] & 2017 & NVIDIA K40 GPU & n/a & 97.37 & n/a \\ \hline DRN + Radical region (b) [48] & 2017 & GeForce GTX 1080 GPU & Caffe & 97.42 & 125.8 \\ \hline DirectMap + ConvNet+ Adaptation [70] & 2017 & NVIDIA Titan X 12G GPU & Theano & 97.37 & 23.5 \\ \hline HCGR-CNN12 Layer + GSLE 4X + ADW [54] & 2017 & single-thread CPU & Caffe & 97.4 & 3 \\ \hline DenseRAN [61] & 2018 & one GPU & n/a & 96.66 & n/a \\ \hline Cascaded model & 2018 & GTX TITAN X GPU & Tensorflow & 97.11 & 3.3 \\ \hline AFL [50] & 2018 & n/a & n/a & 98.29 & 18.2 \\ \hline A novel RAN [62] & 2019 & n/a & n/a & 96.97 & n/a \\ \hline WSAN [51] & 2019 & n/a & n/a & 97.27 & 28.7 \\ \hline HCGR14 Layer + template-instance loss [47] & 2019 & n/a & n/a & 97.45 & n/a \\ \hline MCANet [49] & 2019 & n/a & Pytorch & 97.66 & n/a \\ \hline Inception-ResNet + DWL + Pruning [45] & 2019 & NVIDIA Titan X p GPUs & Caffe & 98.79 & n/a \\ \hline HCGR-CNN12 Layer + pmn + sep [58] & 2019 & NVIDIA Titan X 12G GPU & Caffe & 97.42 & 5.95 \\ \hline Deep matching network [64] & 2020 & GTX1060 (SG) card & Tensorflow & 95.58 & n/a \\ \hline HDE-Net [65] & 2020 & n/a & Pytorch & 97.14 & n/a \\ \hline Melnyk-Net [56] & 2020 & NVIDIA GeForce GTX 1080 & Tensorflow & 97.61 & 24.9 \\ \hline Stroke-based method [66] & 2021 & NVIDIA RTX 2080 Tl & \multirow{2}{*}{Pytorch} & \multirow{2}{*}{96.74} & \multirow{2}{*}{n/a} \\  & GPU with 11 GB memory & & & \\ \hline MSCS + ASA [59] & 2022 & NVIDIA GeForce GTX 2080 & \multirow{2}{*}{Tensorflow} & \multirow{2}{*}{97.63} & \multirow{2}{*}{22.9} \\  & Ti with 11 GB memory & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recognition performance of different deep learning methods and some methods combining traditional techniques on the ICDAR 2013 dataset. “n/a” indicates no mention. Figure 7: Framework of an HCCR method based on concept learning [72]. A discrete dynamic recognition system based on iteration and auxiliary functions employing linear combinations was constructed by [PERSON] et al. [73] in 2020. The algorithm improves the system structure by adding level and translation to construct the font surface, to solve the convergence problem of the flatness of the Chinese character image surface. To accurately recognize handwritten characters, it is critical to learn an appropriate distance metric to measure the difference between data inputs. Existing distance metric learning methods either produce unacceptable error rates or provide results with little interpretability. In 2021, [PERSON] et al. [74] proposed an interpretable distance metric learning method. First, an algorithm named MetChar was proposed to optimize the weight distribution of fixed components. Then, the author proposed an algorithm named HybridSelection to select components and input them into MetChar to learn the distance metric of handwritten Chinese characters. Their work is inferior to the neural network-based approach in recognition accuracy but shows easy interpretation capabilities and good learning efficiency. Figure 8 summarizes the number of papers generated by the HCCR study using the different approaches described in Sections 3.1-3.4 for each year from 2016 to 2022 and shows the annual variation of the highest recognition accuracy achieved on the ICDAR 2013 dataset without model ensemble and data augmentation.