# Burapha-TH: A Multi-Purpose Character, Digit, and Syllable Handwriting Dataset
## 4 Experiments and Discussion  
We performed our study using a desktop computer with an Intel Core i7 3.6 GHz (CPU), 16 GB of memory capacity, and a Nvidia GeForce 1080 Ti VGA card. We used Pytorch 1.1.0 with the Ubuntu 16.04 operating system and Python 3.7. To show the usefulness of our proposed dataset for research in Thai handwriting recognition, we selected three popular CNN architectures: CNN with four convolutional layers, LeNet-5, and VGG-13 with batch normalization. The results are shown in Table 4 to benchmark the proposed dataset. All classifiers were repeated five times by shuffling the training set and averaging accuracy on the test set. The hyper-parameters used to train all the models were batch size 32, dropout 0.5, epoch 100, and optimizer Adam. The testing results show that VGG-13 with BN outperforms the others in terms of accuracy. To measure the VGG-13 BN model's performance on the proposed datasets, we used k-fold cross-validation, with three, five, and ten-fold cross-validation. This technique accounts for the model's variance concerning differences in the training and test datasets and the learning algorithm's stochastic nature. A model's performance can be taken as the mean performance across k-folds, given the standard deviation, which could be used to estimate a confidence interval. We used the scikit-learn API to implement the k-fold cross-validation in our experiment. Figure 8 shows the results of percentage accuracy of all data partitions. Syllable and digit datasets show almost 99%, while the character data shows an average value of about 97%. Figure 7: Examples of handwriting styles included in Burapha-TH. A statistics overview of the public dataset discussed in the paper is compared with Burapha-TH datasets in Table 5. Our proposed datasets have a wider variety of content when compared with other public datasets. The number of writers of Burapha-TH is similar to CASIA-HWDB, which implies that our dataset has a variety of handwriting styles. The training, validation, and test samples are much more extensive than existing Thai handwriting datasets. \begin{table}
\begin{tabular}{l l c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Recognizer**} & \multicolumn{3}{c}{**Recognition Accuracy (\%)**} \\ \cline{3-5}  & & **Training** & **Validation** & **Testing** \\ \hline \multirow{3}{*}{Character} & CNN (4 conv) & 87.34 & 75.28 & 78.51 \\  & LeNet-5 & 88.16 & 75.42 & 78.84 \\  & VGG\_13\_BN & 97.88 & 92.64 & 95.00 \\ \hline \multirow{3}{*}{Digit} & CNN (4 conv) & 94.01 & 83.00 & 83.93 \\  & LeNet-5 & 95.40 & 85.63 & 87.26 \\  & VGG\_13\_BN & 98.51 & 94.03 & 98.29 \\ \hline \multirow{3}{*}{Syllable} & CNN (4 conv) & 83.61 & 75.00 & 78.25 \\  & LeNet-5 & 76.15 & 71.64 & 74.98 \\ \cline{1-1}  & VGG\_13\_BN & 98.41 & 94.40 & 96.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: An overview of the public datasets discussed in the paper and Burapha-TH dataset. Figure 8: Result of BURAPHA-TH dataset ranking K-fold cross-validation. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{1}{c}{**Dataset**} & \multirow{2}{*}{**Language**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Content**} & \multirow{2}{*}{**Class**} & \multirow{2}{*}{**No. of Writers**} & \multicolumn{1}{c}{**Statistics**} \\  & & & & & & **(Train/Validate/Test)** \\ \hline MNIST & EN & 1998 & Digit & 10 & - & 60,000/0/10,000 \\ \hline EMNIST & EN & 2017 & Letter & 26 & \textgreater{}500 & 124,800/0/20,800 \\ \hline CASIA-HWDB & CN & 2011 & \
\begin{tabular}{l} Text, \\ Character \\ \end{tabular} & 7356 & 1020 &
\begin{tabular}{l} 3.5 M isolated \\ character, 1.35 M \\ characters in text \\ \end{tabular} \\ \hline Kuzushij-49 & JP & 2016 & Character & 49 & - & 232,365/03/85,547 \\ \hline Malayam & IN & 2019 & Character & 85 & 77 & 17,236/5706/6360 \\ \hline PE92 & KR & 1992 & Syllable & 2350 & -100 per class \\ \hline SERI95 & KR & 1997 & Syllable & 520 & 465,675/0/51,785 \\ \hline \hline \end{tabular}
\end{table}