Detecting and recognizing seven segment digits using a deep learning approach

Department of Foundation and Diploma Studies, College of Computing and Informatics, Universiti Tenaga Nasional, 43000, Kajang, Selangor, Malaysia

^* Corresponding author: ming@uniten. edu. my

Abstract

Recognizing seven-segment digits is a specific task within the broader field of text detection and recognition. Seven-segment digits are commonly used for displaying numerical information in various applications. However, accurately detecting and recognizing these digits can be challenging due to factors like LED bleeding, glare, and the presence of printed text alongside the digits. The experiment described in this paper aims to identify the most effective models for detecting and recognizing texts and assess their accuracy and performance under different environmental conditions. The experiment reveals that DBNet from PaddleOCR is the best model for text detection, while PARSeq has the best accuracy for recognizing seven-segment digits on the 7Seg dataset. PARSeq also performs well on a custom dataset with lower LED ratios but struggles with glare conditions. Excluding special characters improves accuracy in all conditions.