## 4 Experimental Analysis
### Experimental Result and Analysis
#### 4.3.2 The Necessity of the Sequence Modeling  
To verify the necessity of the sequence modeling, we applied a sequence module to the framework the skip connection structure + CTC for sequence modeling, constructing two models: the model without sequence module, the model with sequence module, then trained and tested on dataset A. The parameters of recurrent layers are the same as in Table 2 and the rest of the two models stay the same for a fair comparison. The performances of the models are summarized in Table 4.  
As shown in Table 4, the test accuracy (_SRA_) of the model without sequence module reached 96.9%, which was 2.1% lower than that of the model with sequence module. It shows that the network model with a sequence modeling part is tested with high accuracy on the dataset with balanced distribution of the training and test sets, which indicates that there is a certain dependency between the feature sequences of instrument readings and the role of sequence modeling of the recurrent layer is necessary. To further understand how RNN help to improve the model performance, we visualized the feature sequence of three samples in the test set A\({}_{2}\) output directly from the CNN layers or from recurrent layers in Figure 9. As indicated in this figure, we observed that the feature sequences from recurrent layers are more distinctive than that directly from the CNN layers, which is more dispersive. This means that RNN can enhance the contextual information among feature sequences, which is very beneficial to model better feature sequences. \begin{table}
\begin{tabular}{c c} \hline \hline
**Network Model** & **Train on A\({}_{1}\)** \\
**Test on A\({}_{2}\)** \\ \hline \hline \multicolumn{2}{c}{_SRA_} \\ \hline The model without sequence module & 0.969 \\
**The model with sequence module** & **0.990** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _SRA_ on dataset A when applying a sequence module or not. 3.3 The Proposed Sequence Module Focuses on Short-Distance Dependencies to Improve Model Generalization  
Except for BasicRNN and LSTM, there are many other RNN evolutionary variants to choose from the RNNs family. For example, Gated Recurrent Unit (GRU) [36] structure removes the cell states and uses hidden states to transfer information with only two gate structures simpler than LSTM, while Simple Recurrent Unit (SRU) [37] reduces front-to-back computational dependencies and enables parallel computation of RNNs. However, they are all good at capturing long-distance dependencies. In order to verify the hypothesis that short-distance dependencies is better than long-distance dependencies in the multi-type digital instrument reading recognition task, where the characters at each position of the reading string are relatively independent from each other. We applied LSTM, GRU and SRU for sequence modeling to compare with our BasicRNN-adopted sequence modeling module. Then we trained and tested the BasicRNN-adopted model, the LSTM-adopted model, the GRU-adopted model and the SRU-adopted model on dataset B, which is designed for invisible data. The test results of the four models for the \(SRA\) and the number of memorial errors are shown in Table 5.  
In Table 5, it was shown that the performance of the BasicRNN-adopted model presented a significant improvement compared with the other three models. The BasicRNN-adopted model achieved an 89.7% accuracy. This was 8.2%, 5.8% and 8.9% higher than the accuracy of the LSTM-adopted model, the GRU-adopted model and the SRU-adopted model, respectively. Looking at it another way, there were 44, 36, and 35 memorial errors for the three models respectively, much more than that of the BasicRNN-adopted model. This fully proved that LSTM, GRU and SRU were good at establishing long-distance dependencies and prone to endorsement style memorial errors. The test results indicate that  
Figure 9: Heat map of the feature sequence output from sequence module or not.