# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 5 Discussion
### Methods Comparison  
Tables 6 and 7 show the advantages and disadvantages of the different methods for recognizing offline HCC and offline HCT, respectively. For character recognition, compared to extracting features with traditional techniques and then using machine learning classifiers, deep learning methods can achieve end-to-end recognition in this enormous class classification task. Along with the continuous improvement of neural network models, deep learning-based methods have achieved good recognition performance in HCCR even beyond that of humans and are now the dominant methods in the field. Some new techniques that combine other concepts and focus more on small samples have inferior recognition accuracy compared to deep learning methods but can be easily interpreted and have good learning efficiency. The focus of text recognition methods is gradually shifting from segmentation-based to segmentation-free based that can provide good end-to-end recognition performance. Combining the advantages of both, i.e., achieving end-to-end recognition while providing character location information, has also emerged in the last two years to show effectiveness. Combining models with writer awareness and language models is also a common practice in this field to improve recognition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Type** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{Traditional technology} & Low hardware requirement; & Difficult to capture advanced semantic features; \\  & Relatively easy to implement; & Poor accuracy; \\  & Good effect for specific scenes; & Poor generalizability and robustness. \\  & Greater interpretability. & \\ \hline \multirow{3}{*}{Deep learning} & End-to-end training available; & Highly influenced by dataset; \\  & High accuracy; & High computational requirement; \\  & Stronger generalizability and robustness. & Weak interpretability. \\ \hline \multirow{3}{*}{Traditional techniques combined with deep learning} & The features extracted by traditional techniques can be & \\  & added with prior knowledge; & The steps are relatively independent and lack a global \\  & Fusion classifiers with neural networks and machine & optimization scheme for control; \\  & learning classification algorithms can be used to improve & Less efficient. \\  & recognition accuracy. & \\ \hline \multirow{3}{*}{Other concept} & Research can be based on small samples; & \\  & Easy to interpret and has good learning efficiency. & The recognition accuracy is inferior. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of the advantages and disadvantages of methods for recognizing offline HCC.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Type** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{Segmentation} & Provides the detection information & The segmentation effect is affected by \\  & of individual characters. & character overlap and adhesion, \\  & & which limits recognition accuracy; \\  & Over-segmentation faces the problem of sparse data. \\ \hline \multirow{3}{*}{Segmentation-free} & Avoid costly character segmentation annotations & \\  & and segmentation errors to achieve good & Loss of character location information. \\ \cline{1-1}  & end-to-end recognition. & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of the advantages and disadvantages of methods for recognizing offline HCT.