GUI-Based Maintenance Reporting System for Electrical Substations Integrated with PyTesseract OCR

Abstract

This work focuses on developing a graphical user interface (GUI) system integrated with Optical Character Recognition (OCR) for generating electric substation maintenance reports. The main goal is to automate the process of extracting temperature data from thermal images captured during Condition-Based Monitoring (CBM) tasks using FLUKE equipment. PyTesseract, an OCR engine in Python, reads annotated temperature values such as SP1, SP2, and AVG from the thermal images. The GUI is developed using Tkinter and OpenCV, allowing users to load images, perform OCR on the whole image or by selecting a Region of Interest (ROI), and validate extracted readings. The validated data is then inserted into a structured table and exported into a Microsoft Word report. Evaluation shows that ROI-based OCR significantly improves accuracy compared to whole-image OCR. This system offers a more efficient, user-friendly, and accurate approach for substation data reporting, reducing human error and improving documentation speed for maintenance workflows.