# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 2 Databases and Model Evaluation Metrics
### Standard Databases  
Data play a critical role in pattern recognition, and a high-quality dataset can improve the effectiveness of model training and the accuracy of prediction results. Table 1 summarizes the commonly used publicly available datasets for offline handwritten Chinese recognition and gives detailed descriptions. To facilitate the study, the corresponding download links are provided in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Year** & **Total Sample** & **Content** & **Writers** & **Description** \\ \hline HIT-MW [10] & 1996 & 500,370 & \begin{tabular}{c} 3755 Chinese characters and \\ 94 common symbols, punctuation, \\ English letters, etc. \\ \end{tabular} & 130 & \begin{tabular}{c} The samples written by 50 people are \\ segmented, and the samples written \\ by 80 people are not segmented. \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{c} SCUT- \\ IRAC [11] \\ \end{tabular} } & \multirow{4}{*}{2006} & \multirow{4}{*}{186,444} & 8664 lines of text (including letters, \\  & & punctuation and Chinese \\  & & characters), each sample has \\  & & & 10.16 text lines, each text line has \\  & & & 21.51 characters, each sample \\  & & & includes 218.57 characters \\ \hline \multirow{4}{*}{\begin{tabular}{c} CASIA- \\ HWDB \\ 1.0-1.2 [12] \\ \end{tabular} } & \multirow{4}{*}{2010} & \multirow{4}{*}{3,900,000} & 7356 classes (7185 Chinese \\  & & characters and 171 symbols) & 1020 & \begin{tabular}{c} At the character level, all of the data \\ have been segmented and annotated, \\ and each dataset has been partitioned \\ into standard training and test subsets. \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{c} CASIA- \\ HWDB \\ 2.0-2.2 [12] \\ \end{tabular} } & \multirow{4}{*}{2010} & \multirow{4}{*}{1,350,000} & 5090 pages & 1020 & \begin{tabular}{c} At the character level, all of the data \\ has been segmented and annotated, \\ and each dataset has been partitioned \\ into standard training and test subsets. \\ \end{tabular} \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} SCUT- \\ COUCH [13] \\ \end{tabular} } & \multirow{4}{*}{2011} & \multirow{4}{*}{2011} & \multirow{4}{*}{2012} & 5401 Big5 traditional Chinese characters, 1384 traditional Chinese characters corresponding to GB2312-80 level 1 font, \\  & & & 44,208 phrases, 2010 Chinese pinyin, \\  & & & 184 symbols (including letters, \\  & & numbers, and common symbols), \\  & & & 8809 lines of online text \\ \hline \multirow{2}{*}{
\begin{tabular}{c} ICDAR \\ 2013 [14] \\ \end{tabular} } & \multirow{2}{*}{2013} & \multirow{2}{*}{224,419} & \multirow{2}{*}{3432 text image samples} & \multirow{2}{*}{60} & Each image has the word’s bounding \\  & & & & box label and text content. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Introduction of some commonly used handwritten Chinese datasets. The first offline HCT dataset is HIT-MW [10], established by the Harbin Institute of Technology in 2006. It contains a total of 853 handwritten text images written by 780 people and 186,444 Chinese character samples. The corpus data were obtained from reports published by the People's Daily. The Chinese characters in this dataset were written in various styles, and the images contain scribbles and skewed lines of text. Therefore, this database is mainly used for document segmentation, HCCR, and HCTR tasks. All images in the database are stored as binary images for researchers to use. The CASIA-HWDB dataset [12], subsequently collected by the Institute of Automation of the Chinese Academy of Sciences, is the primary publicly available and widely used dataset in research on offline handwritten Chinese recognition. The CASIA-HWDB dataset contains six sub-datasets, divided into the character sets HWDB1.0-1.2 and text sets HWDB2.0-2.2. Character sets 1.0-1.1 contain commonly used Chinese characters, and 1.2 is a non-useful Chinese character dataset. The three datasets contain 7356 characters (7185 Chinese characters and 171 other symbols) in nearly 3.9 million images.