# Deep Learning-Powered System for Real-Time Digital Meter Reading on Edge Devices
## 1 Introduction  
The automatic reading of water treatment station digital equipment is an emerging need for the real-time monitoring of important parameters related to the oxidation-reduction potential (ORP), dissolved oxygen (OD) and flow rate. The reading process is typically performed manually on-site by operators who register the values and later insert them into the company's system. This operation is prone to errors, time-consuming and has low efficiency. Moreover, no photo is stored in the system for evidence. Although one possibility can be the replacement of the devices with a digital version capable of transmitting the readings, it involves unnecessary costs besides not contributing to the sustainability of the current devices, so automation is the key to optimizing this task by making it easier, more effective and auditable. Artificial intelligence (AI) has increasing potential to enhance the integration of knowledge on the ground by providing tools to operators which facilitate performing maintenance work. The task of automatic reading of digital equipment is frequently addressed by a process named automatic meter reading (AMR). In recent years, many AMR approaches have been proposed in the literature, typically comprising the detection of the reading area and optical character recognition (OCR). In OCR, a text localization algorithm is applied to the reading area, and the text provided by the detector is recognized. The dominant trend for AMR nowadays is using deep learning-based algorithms. [PERSON] et al. [1] proposed a convolutional neural network (CNN)-based two-stage approach that employs the Fast-YOLO [2] object detector for counter detection and tests three different methodologies for number recognition: CR-NET [3], multi-task learningand convolutional recurrent neural network (CRNN) [4]. The authors also took advantage of data augmentation to balance the data and increase robustness. The Fast-YOLO model successfully detected the counter in all images and, for counter recognition, CR-NET outperformed both multi-task and CRNN models. [PERSON] et al. [5] proposed an AMR pipeline comprising the two previously mentioned stages and introduced a new corner-detection and counter-classification module with the aim to rectify the detected counter as well as rejecting illegible meters, prior to the recognizer module. Their approach describes impressive end-to-end recognition rates when dismissing the readings made with lower confidence values. [PERSON] et al. [6] developed a highly robust deep learning method for reading recognition of the digital multimeter. Firstly, the digital display area is detected with the YOLOv5 model [7], followed by performing character segmentation based on the prior knowledge that the display accommodates five character areas. The combination of the output from a CNN character recognition model for each area (0-9 numbers, negative signs and blanks) with decimal point positioning results produces the complete reading information. This method reaches very high accuracy values and is able to adapt to complex environments; however, it does not work for other types of equipment. [PERSON] et al. [8] presented a different approach by training an end-to-end CNN to directly issue meter readings without locating the relevant text. Although the authors reported promising results, this approach was tested on close-up images with the counter centered. This work is developed in the scope of the SINATRA project that aims to provide new insights and develop a support solution for Industry 4.0. This work aims to improve the reliability and traceability of meter readings, while reducing human effort through the usage of a simple mobile application running on edge devices in real-time. We propose a new AI-based framework that supports intelligent data acquisition and provides automated detection and recognition of digits on the counter. A standard AMR approach is followed by first identifying the screen of the equipment and then extracting the values that must be tracked.