Robust OCR Pipeline for Digit Display Recognition Using TrOCR, YOLOv8, and Multi-Layered Fallbacks

The variable lighting conditions, segmentation complexity, and inconsistent formatting of digital displays, like those found on utility meters and fuel pumps, present ongoing challenges for optical character recognition (OCR) systems. For ROI detection, we suggest a reliable, multi-model OCR pipeline that combines a refined TrOCR model with YOLOv8 and is enhanced with fallback mechanisms utilizing Tesseract and EasyOCR. Numerical output integrity is improved by a custom decimal correction procedure. After post-processing, our suggested approach outperforms standalone OCR engines by achieving a 97% success rate on real-world digit displays. We examine failure cases from previous CNN-based segmentation attempts, present comparative performance analysis, and describe upcoming work for wider deployment.