## 4 Experimental Analysis
### Datasets and Implementation Details
#### 4.1.2 Implementation Details  
The network configuration of the proposed method is summarized in Table 2. The architecture of the feature extractor is inspired from the VGG architecture [35] and the residual network. Some improvement is made in order to make it suitable for recognizing the variable-length reading of muti-type digital instruments. To reduce the computational burden, the first convolutional layer uses 64 filters with a (3,3) kernel size and stride of (2,2). The kernel size and stride for the pooling layer are set to (2,2) and (1,2) respectively, for two reasons. On the one hand, there will be overlap and coverage between the output features in the horizontal direction, which can enhance the feature richness, on the other hand, the stride set to (1,2) can produce a rectangular perceptual field with narrow width, and it is easier to recognize small characters with narrow width, such as \"...\"1\", etc. To ensure that the height dimension of the output feature sequence is 1 and that the number of feature sequences is appropriate (much larger than the number of characters in the reading string), while retaining more spatial information. The first two residual modules use convolutional layers with stride of (1,2) to down-sample only the height of the feature map, while keeping the width unchanged. Then, the last module performs downsampling \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Number of** \\ **Training Samples** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Testing Samples** \\ \end{tabular} & \begin{tabular}{c} **Notes** \\ \end{tabular} \\ \hline A & 4213 & 412 & \
\begin{tabular}{c} Containing 1–6 digits images, the testing data and training data are in equilibrium distribution on the kinds of strings, character fonts, character spacing and aspect ratios. \\ \end{tabular} \\ \hline B & 4150 & 421 &
\begin{tabular}{c} Containing 1–6 digits images, about 23\% of the reading strings in the test set were not found in the training set, while 77\% pictures in testing data are quite different from training data for character fonts or character spacing and aspect ratios. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The datasets distribution used in our experiment. Figure 7: Some raw images and some samples in the basic dataset. in both the height and width directions by convolutional layer with stride of (2,2). It should be noted that the downsampling only happen in their first convolutional layer. In the same residual module, each convolutional layer has the same number of filters. The number of filters is doubled one by one for each of the three residual blocks. In addition, we use a convolutional layer with a (1,1) kernel size and stride of (1,2) to reduce the number of parameters while achieving cross-channel interaction and information integration. In the end, between two feature sequences, the last a convolutional layer with a (2,1) kernel size and stride of (1,1) is adopted to strengthen the adjacent connection in the feature map. Finally, the new 512-dimensional feature sequences with a height of 1 are generated as the input of the recurrent layer. The network is implemented within the tensorflow1.14 deep learning framework under Python 3.7. Experiments are carried out on a workstation with Intel(R) i7 processor, RTX1070 Ti discrete graphics card, 32G RAM. The AdaDelta is chosen to update the network parameters, while the learning rate is set to 0.0001. ReLu is used as the activation function of each layer, and CTC_loss is used as the loss function to achieve end-to-end network training with a total of 200 epochs. In order to speed up the training process, each convolutional layer is followed by a BN layer. It is found that the value of batch_size has a great impact on the results. If batch_size is too small, training process is time-consuming and bad for convergence. If batch_size is too large, it will easily fall into the local minimum. Therefore, batch_size was finally set to 32, after several training comparisons.