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

Abstract

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.

Introduction

This paper addresses the challenges of recognizing digits on low-contrast, overlapping, or misaligned real-world displays (e.g., fuel pumps, utility meters) where traditional OCR tools like Tesseract often fail. To improve reliability, it proposes an end-to-end OCR pipeline combining YOLOv8 for detecting digit regions, a fine-tuned TrOCR model for recognition, and fallback OCRs (Tesseract, EasyOCR) with a decimal correction mechanism to handle common errors.

The system uses data augmentation and preprocessing (e.g., perspective correction, contrast enhancement) to boost accuracy. YOLOv8 achieved high recall (94.4%) in detecting digit regions but struggled with glare or unusual layouts. TrOCR, fine-tuned on a custom dataset of 2,500 images, outperformed fallback OCRs, reaching a post-processed accuracy of 97%. Decimal error handling improved numeric formatting by over 10%.

Performance optimization included GPU acceleration and batch processing. Failure modes were mainly due to difficult ROI detection and occasional OCR misreads. Limitations include difficulties with severely distorted displays and computational demands limiting deployment on low-end devices.

The study emphasizes the importance of fallback mechanisms and error correction for real-world reliability, discusses ethical concerns like data privacy and GDPR compliance, and suggests future improvements with lightweight models and synthetic data augmentation.

Conclusion

We demonstrated a robust OCR pipeline that achieved 97% accuracy on real-world digit displays by combining TrOCR, YOLOv8, and fallback OCRs with decimal correction. With plans to update results by the end of 2025, future work will involve exploring generative OCR refinement, optimizing for edge devices, and growing the dataset.

References

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Copyright

Copyright © 2025 Deon Menezes, Sajal Patel, Abdul Shaikh, Nishaad Parikh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.