# Deep Learning-Powered System for Real-Time Digital Meter Reading on Edge Devices
## 3 Results and Discussion
### Automatic Meter Reading Pipeline
The model is not able to perceive if a second text box is present when in reality it is, thus not returning any result. Additionally, there are no ground truth labels for 46 ORP and 13 OD values because it was not possible for the human annotator to discriminate the actual values, as a consequence of the previously mentioned artifacts. Another common problem found in the images of the UC was a malfunction in the display, which affected the readability of some values. A few cases related to text detection and template matching issues are demonstrated in the first row of Figure 6, whereas the second row shows difficult cases for text recognition. Figure 7 presents examples of outputs obtained at the end of each module of the pipeline, identifying their importance in subsequent steps. Figure 7a,d show the correct functioning of all stages of the pipeline. The correct definition of the screen corners is of paramount importance; otherwise, the graphical structure of the display area will not correspond as expected, affecting template matching and consequently the output of the system, as illustrated in Figure 7b,e. Faulty text detection has a strong negative impact on text recognition: the negative sign is often left out by the detector, or other symbols are also considered, thus making it impossible for the recognizer to correctly identify the actual value (Figure 7c). The framework also reveals difficulties when glare or reflections  
Figure 6: Examples of challenging images of universal controllers (UC) with corrected perspective from the test set. The first row demonstrates cases where the text detection—(**a**,**b**)—and template matching—(**c**,**d**)—module cannot infer results, the second row presents difficult cases for the text recognition module—(**e**–**h**). are present in the images, as they overshadow the values and introduce noisy patterns (Figure 7f). To evaluate the execution time of the system, a Samsung Active Tab3 was used (the same device used by the operators in the field due to its robustness and safety). Although it is a device equipped with hardware present in older devices, making it comparable with current low-end devices, the system performed within reasonable timing. The step of screen detection that runs solo in each preview frame takes under a quarter of a second on average, making it visually acceptable to the end user. With the acquired photo, the user needs to wait, on average, one and a half seconds to read the extracted values, whereas the text detection model takes most of the time (a bit over one second). The timings were obtained (see Table 6) from the average of several acquisitions with the mentioned device. To the best of our knowledge, we are not aware of any works in the literature for the same type of devices, thus not allowing a direct comparison. The most similar AMR systems are proposed for analog energy meters or multimeters as described in Section 1; however, they are quite different from the equipment in our work. The system introduced by [PERSON] et al. [5] performed the correct reading of 94.75% and 96.98% of the meters in their UFPR-AMR [1] and Copel-AMR [5] test sets. [PERSON] et al. [8] describes an overall end-to-end recognition accuracy of 94.167%, while [PERSON] et al. [11] report accuracy of 98% on private datasets. The results reported by these works are quite superior when compared to our approach, which is misleading considering that these images show excellent conditions of acquisition and a lack of challenging artifacts. Figure 7: Examples of the results obtained by the modules of the proposed pipeline. The first row contains the central cropped images; the second row presents the resulting images of the screen bounding box model and keypoints detected by the screen keypoint model; the third row includes the important text boxes and corresponding text identified by the text detection and text recognition modules, respectively.