# An End-to-End Classifier Based on CNN for In-Air Handwritten-Chinese-Character Recognition
## 4 Experiments
### Comparison Experiments
However, this strategy will exponentially increase the storage cost and the improvement in recognition accuracy is not ideal. As shown in Table 3, the recognition accuracy of RNN2-Ensemble is 0.8% higher than that of RNN2, but the storage cost is about 3.67 times that of RNN2. Compared with RNN, end-to-end CNN can extract more discriminate spatiotemporal features more comprehensively, requires only a single model and the storage cost is only 6.48 MB. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Accuracy (\%)** \\ \hline padding1 & 96.07 \\ padding2 & 96.0 \\ padding3 & 95.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy of different padding methods on the IAHCC-UCAS2016 dataset. Figure 7: The recognition accuracy of different zero-pad methods on the IAHCC-UCAS2016 varies with the training epoch. We also compared our proposed method with traditional methods on both datasets. These methods include nearest prototype classifier (NPC) [36], nearest prototype classifier trained by MCE (NPC-MCE) [28], multistage classifiers (Multi1) [29], discriminative multistage classifiers (Multi2) [26], modified quadratic discriminant functions (MQDF) [25], locality-sensitive sparse representation-based classifiers (LSRC) [27] and locality-sensitive sparse representation toward optimized prototype classifier (LSROPC) [29]. Table 5 and 6 summarize the recognition performance of the various methods on both datasets. From Table 5 and 6, compared with traditional machine-learning methods, deep-learning technology has huge advantages, and the above methods all identify the features extracted by artificial means, which inevitably loses the timing information of the trajectory sequence to a certain extent. Therefore, using CNN to directly identify coordinate sequences can achieve an overwhelming performance improvement. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy (\%)** & **Storage (MB)** & **DA** & **Ensemble** \\ \hline NPC [36] & 91.30 & 4.76 & No & No \\ NPC-MCE [28] & 92.96 & 4.76 & No & No \\ Multi1 [29] & 92.36 & 124.5 & No & No \\ Multi2 [26] & 93.42 & 124.5 & No & No \\ MQDF [25] & 95.21 & 184.84 & No & No \\ LSRC [27] & 94.40 & 27.86 & No & No \\ LSROPC [29] & 95.50 & 65.53 & No & No \\ Proposed method & 98.02 & 6.44 & No & No \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of various methods on the GBI dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy (\%)** & **Storage (MB)** & **DA** & **Ensemble** \\ \hline Cov8d [23] & 91.62 & 20.2 & No & No \\ CovCd1 [23] & 92.32 & 20.2 & Yes & No \\ CovCd2 [23] & 92.93 & 20.2 & Yes & No \\ RNN1 [20] & 92.50 & 7.03 & No & No \\ RNN1-Ensemble [20] & 93.40 & 61.59 & No & Yes \\ RNN2 [4] & 93.60 & 7.01 & No & No \\ RNN2-Ensemble [4] & 94.40 & 25.75 & No & Yes \\ Proposed method & 96.07 & 6.48 & No & No \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of different deep-learning methods on the IAHCC-UCAS2016 dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy (\%)** & **Storage (MB)** & **DA** & **Ensemble** \\ \hline Cov8d [23] & 96.10 & 19.4 & No & No \\ CovCd1 [23] & 97.15 & 19.4 & yes & No \\ CovCd2 [23] & 97.43 & 19.4 & yes & No \\ Proposed method & 98.02 & 6.44 & No & No \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of different deep-learning methods on the GB1 dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Accuracy (\%)** & **Storage (MB)** & **DA** & **Ensemble** \\ \hline NPC [36] & 86.90 & 8.52 & No & No \\ NPC-MCE [28] & 88.93 & 8.52 & No & No \\ Multi1 [29] & 88.28 & 128.8 & No & No \\ Multi2 [26] & 88.90 & 128.8 & No & No \\ MQDF [25] & 89.96 & 191.11 & No & No \\ LSRC [27] & 88.93 & 31.78 & No & No \\ LSROPC [29] & 91.01 & 70.00 & No & No \\ Proposed method & 96.07 & 6.48 & No & No \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of various methods on the IAHCC-UCAS2016 dataset.