One of the important steps in digital transformation is to make all the instrumental devices connected to the cloud. However, replacing the existing analogue meters with the cloud-connected digital meters can be very costly especially for industrial grade meters. In this project, a deep learning model based on Single Shot Detector (SSD) MobileNet V2 and an optical character recognition (Tesseract OCR) engine are developed for the low-cost digitization of analogue meter readings. The deep learning model is trained with a dataset of 750 meters' images, and it is used to detect the region of interest where the meter's readings are located. The OCR is used to convert the readings to string datatype. The image processing techniques via the OpenCV library are adopted for enhancing the quality of the region of interest (ROI). The model is developed in Python and the evaluation is carried for various types of meters, illumination conditions, and backgrounds. The results show that the deep learning model and OCR accuracies are 95% and 93%, respectively.