# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 5 Discussion
### Challenges and Future Work  
As seen from Figures 8 and 10, the current accuracy of offline HCCR and offline line-level HCTR with and without LM reach over 98%, 97%, and 94%, respectively, which is close to or even exceeds human recognition performance in terms of accuracy. However, considering the practical applications of handwritten Chinese recognition, some issues in this field still deserve attention and research. 1. Offline handwritten Chinese recognition in different scenarios. The recognition objects of current research are mostly characters and text in simple scenarios. However, in practical life, we must process handwritten Chinese in more complex scenes influenced by lighting, shooting, and different applications. 2. Create unconstrained handwritten datasets with large categories and multiple styles. Adequate and diverse data are essential for designing and training handwritten Chinese recognition models. Therefore, more unconstrained handwriting databases containing large classes and various styles are currently needed. For most current experiments, researchers have only trained and tested models on two public  
Figure 11: Research questions focused on handwritten Chinese recognition from 2016 to 2022. The blue circle represents research on HCCR, and the purple circle represents research on HCTR. The size of each sub-circle and label represents the number of studies on the issue. datasets, CASIA-HWDB and ICDAR 2013. However, they still do not contain a large enough variety of Chinese characters and sample size. 3. Ultra-large category offline handwritten character recognition. The current studies focus on recognizing the 3755 classes of Chinese characters commonly used in the GB 2312-80 regulation, but the total number of Chinese characters exceeds 50,000. In the future, the objects of ultra-large category recognition can include more categories of simplified and traditional Chinese characters and English, Japanese, and other significant languages commonly used worldwide to achieve universal recognition of offline handwritten characters. 4. Unconstrained offline page-level handwritten Chinese text recognition. Most current studies on offline HCTR focus on line-level text that only focuses on the recognition performance of cropped text lines. The few studies on page-level HCTR are also limited to pages with specific formats, such as ancient books or standard test papers. However, modifications and random irregular writing can significantly affect the recognition results in practical application scenarios. 5. Create fast and compact practical models. Research on handwritten Chinese recognition based on deep learning often accompanies many parameters that consume much time to train the model. This makes deploying the models in embedded systems such as Raspberry Pi and cell phones challenging. To facilitate further work, Table 8 summarizes the existing challenges, applications, and future research on handwritten Chinese recognition, expecting to provide some references for researchers. \begin{table}
\begin{tabular}{c c c} \hline
**Existing Challenges** & **Applications** & **Future Research** \\ \hline \multirow{6}{*}{Less research on recognition tasks in complex scenes} & Recognize handwritten medical manuscripts with & \\  & adherent or simplified characters; & \\  & Recognize irregular or mutilated characters written by & \\  & people with functional impairment; & \\  & Recognize similar characters in jobs requiring & \\  & high-precision recognition; & \\  & Recognize handwritten characters in natural scenes & \\  & under different lighting and photo angles. & \\ \hline \multirow{6}{*}{Lack of datasets containing sufficient variety and number of samples} & Designing and training recognition models using unconstrained handwriting datasets with large categories and multiple styles to adapt to recognizing handwritten & \\  & Chinese in different scenarios.