Enhancing Water Meter Digit Recognition Using YOLO-OCR
Received: 1 January 2026 | Revised: 22 February 2026, 7 March 2026, and 11 March 2026 | Accepted: 15 March 2026 | Online: 6 June 2026
Corresponding author: Sudianto Sudianto
Abstract
This study presents a deep learning-based framework for automatic water meter digit recognition by combining You Only Look Once version 8 (YOLOv8) for digit region detection and PP-OCRv4 for Optical Character Recognition (OCR). The model was developed using a dataset of 3,250 annotated water meter images collected under various lighting and visibility conditions, including ideal and non-ideal scenarios. The detection module was fine-tuned using different optimizers, with Adam achieving the highest detection accuracy (F1-score, precision, and recall all at 100% and a Mean Average Precision at Intersection over Union (IoU) thresholds ranging from 0.50 to 0.90 (mAP50–90) of 0.95831). The recognition model was trained on both the full dataset and a subset of 1,000 non-ideal images. Interestingly, the best performance was observed in the model trained solely on non-ideal data using AdamW, achieving a recognition accuracy of 90.90% and a Character Error Rate (CER) of 0.0151 in end-to-end testing across 55 benchmark images. The system demonstrated near real-time inference, averaging 0.5 s per image. These results validate the robustness and practical applicability of the proposed YOLO-OCR pipeline and highlight the advantage of training on visually challenging data to improve robustness in real-world deployment conditions.
Keywords:
YOLOv8, PP-OCRv4, water meter, detection, recognitionReferences
K. Satija and M. Chhatlani, "Infrastructure & Industrialization: An SDG-9 Study of Gujarat," International Journal of Science and Social Science Research, vol. 3, no. 1, pp. 310–315, June 2025.
A. J. Zapata-Sierra, E. Salmerón-Manzano, A. Alcayde, M. L. Zapata-Castillo, and F. Manzano-Agugliaro, "The Scientific Landscape of Smart Water Meters: A Comprehensive Review," Water, vol. 16, no. 1, Dec. 2023, Art. no. 113.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 779–788.
Sudianto, Y. Herdiyeni, A. Haristu, and M. Hardhienata, "Chilli Quality Classification using Deep Learning," in 2020 International Conference on Computer Science and Its Application in Agriculture, Bogor, Indonesia, 2020, pp. 1–5.
B. Satya, D. Manongga, Hendry, and A. Aminuddin, "Optimized YOLOv8 for Automatic License Plate Recognition on Resource Constrained Devices," Engineering, Technology & Applied Science Research, vol. 15, no. 2, pp. 21976–21981, Apr. 2025.
Y. Du et al., "PP-OCR: A Practical Ultra Lightweight OCR System." arXiv, Oct. 15, 2020.
F. Khan, S. Rafique, and G. M. Khan, "Low-Cost Smart Metering Using Deep Learning," International Journal of Innovations in Science & Technology, vol. 6, no. 5, pp. 93–104, May 2024.
B. Nguyen van et al., "Water Meter Reading Based on Text Recognition Techniques and Deep Learning," IEEE Access, vol. 13, pp. 41422–41434, 2025.
H. Moussaoui et al., "Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition," Scientific Reports, vol. 14, no. 1, June 2024, Art. no. 14389.
M. Imran, H. Anwar, M. Tufail, A. Khan, M. Khan, and D. Ramli, "Image-Based Automatic Energy Meter Reading Using Deep Learning," Computers, Materials & Continua, vol. 74, no. 1, pp. 203–216, Sept. 2022.
M. Yaseen, "What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector." arXiv, Aug. 28, 2024.
V. Afifah and S. Erniwati, "YOLOv8 for Object Detection: A Comprehensive Review of Advances,Techniques, and Applications," IJACI : International Journal of Advanced Computing and Informatics, vol. 2, no. 1, pp. 53–61, Jan. 2026.
C. Cui et al., "PaddleOCR 3.0 Technical Report." arXiv, July 08, 2025.
T.-Y. Lin et al., "Microsoft COCO: Common Objects in Context." arXiv, Feb. 21, 2015.
H. Zhang and X. Yang, "A Survey on Algorithms and Performance Metrics for Object Detection," International Core Journal of Engineering, vol. 9, no. 9, pp. 133–145, Sept. 2023.
R. Taori, A. Dave, V. Shankar, N. Carlini, B. Recht, and L. Schmidt, "Measuring Robustness to Natural Distribution Shifts in Image Classification," in 34th Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, pp. 18583–18599.
K. Zhou, Z. Liu, Y. Qiao, T. Xiang, and C. C. Loy, "Domain Generalization: A Survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 4, pp. 4396–4415, Apr. 2023.
S. Wang, R. Veldhuis, C. Brune, and N. Strisciuglio, "A Survey on the Robustness of Computer Vision Models against Common Corruptions." arXiv, Sept. 14, 2024.
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Copyright (c) 2026 Fadhila Alya Syahfahlevi, Sudianto Sudianto, Aminatus Sa'adah
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