# Deep Learning-Powered System for Real-Time Digital Meter Reading on Edge Devices
## 3 Results and Discussion
### Automatic Meter Reading Pipeline  
The proposed system achieves an overall end-to-end recognition accuracy of 55.47% and 63.70% for the ORP and OD values of the UC, respectively, and 73.08% for the flow rate values of the FM, as depicted in Table 6. Regarding the metrics for screen detection, the values are different than the ones previously reported due to slight differences in the conversion to the ONNX format. The deterioration in the performance of the OCR models, text detection and text recognition, is noticeable and related to the propagation of errors from the prior modules, as we will explain next. As described in Section 2.3, when the detection modules are not able to locate the screen or text boxes, or when the detected text regions fail template matching, meaning that the regions do not have sufficient overlap with the expected position, the pipeline \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Module**} & \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**Equipment**} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **Execution Time** \\ **(ms)** \\ \end{tabular} }} \\  & & **UC** & & **FM** & **(ms)** \\ \hline Screen Bounding Box Detection & mAP00.5 & 79.65 & 56.11 & \\  & mAP00.5 & 0.95 & 64.27 & 36.60 & 230 \\ \hline Screen Keypoint Detection & \begin{tabular}{c} ICU \\ ED \\ \end{tabular} & \begin{tabular}{c} \(93.78\pm 2.54\) \\ \(7.85\pm 5.75\) \\ \end{tabular} & \begin{tabular}{c} \(87.26\pm 4.77\) \\ \(10.36\pm 4.92\) \\ \end{tabular} & 96 \\ \hline Text Detection & \
\begin{tabular}{c} Recall \\ Precision \\ F-Measure \\ \end{tabular} & 41.45 & 83.82 & \\  & F-Measure & 40.02 & 42.31 & 1055 \\ \hline Text Recognition & Accuracy &
\begin{tabular}{c} ORP: 55.47 \\ OD: 63.70 \\ \end{tabular} & 73.08 & 160 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results for the pipeline modules in the ONNX format. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Input Size** & **Training** & **Equipment** & **Accuracy** \\ \hline \multirow{4}{*}{Rosetta} & \multirow{4}{*}{\(32\times 100\)} & Pre-trained & \
\begin{tabular}{c} UC \\ FM \\ \end{tabular} & 67.95 \\  & & FM & 84.62 \\ \cline{2-5}  & & Fine-tuned on & UC & 78.90 \\  & & SINATRA & FM & 92.31 \\ \hline \multirow{4}{*}{CRNN} & \multirow{4}{*}{\(32\times 128\)} & Pre-trained &
\begin{tabular}{c} UC \\ FM \\ \end{tabular} & 80.27 \\  & & FM & 89.23 \\ \cline{1-1} \cline{2-5}  & & Fine-tuned on & UC & 87.67 \\ \cline{1-1} \cline{2-5}  & & SINATRA & FM & 89.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for recognition of important text (text recognition module). does not advance to the next step. Considering the UC device, the screen bounding box detection model is tested in 222 images. This stage failed to locate the screen in 9% (20 images), mainly due to low detection confidence. Regarding the text detection and template matching module, 202 cases are received, no text is detected in 0.5% (1 image), or both text boxes fail to overlap with their expected position in nearly 5% (11 images). In the remaining 190 samples, 183 boxes related to ORP and 160 of OD are successfully located. However, as the text is not readable in a number of cases, 137 ORP and 146 OD boxes advance to the final step, with 55.47% (76 images) and 63.70% (93 images), respectively, being correctly distinguished by the text recognizer. For the FM equipment, the screen bounding box detector is tested in 68 images, failing in 10% (7 images). Hence, 61 cases go through the text detection model, which always locates text boxes; however, the template matching step failed in 15% of the samples (9 images). Due to the previously identified fails in previous steps of the pipeline, 76% of FM cases (52 images) achieved the text recognition module, of which 73.08% (38 images) are veracious. The different number of text boxes for ORP and OD in the UC obtained by the text detection module is explained by underexposure or occlusions provoked by glare and reflections.