SavedAdd your journal

Journals / Gazi University Journal of Science Part A: Engineering and Innovation / 2026 / Volume: 13 Issue: 3

Automated Expiration Date Extraction from Official Documents: A Real-Time OCR-Based Mobile System

Mehmet Tat(DUZCE UNIVERSITY)0009-0001-3494-2798
Büşra Takgil(DÜZCE ÜNİVERSİTESİ)0000-0002-7927-0083
Pages
—
Publication Date
2026-09-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Although there are some structural limitations in analyzing complex structures and handwriting that require human verification, the findings demonstrate that DocGuard offers a scalable, privacy-preserving digital document lifecycle infrastructure applicable across a wide range of areas, from corporate compliance processes to fintech KYC verifications.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Forgetting the expiration dates on official documents can lead to serious problems.
📊
Key Findings & Data:Margin of error and security vulnerabilities in the management of document validity processes constitute significant risk factors.
💡
Conclusion & Impact:Although there are some structural limitations in analyzing complex structures and handwriting that require human verification, the findings demonstrate that DocGuard offers a scalable, privacy-preserving digital document lifecycle infrastructure applicable across a wide range of areas, from corporate compliance processes to fintech KYC verifications.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Client-Side Encryption · Document Lifecycle Management · Mobile Edge Computing

Abstract

Forgetting the expiration dates on official documents can lead to serious problems. These problems can be both legal and financial, and they can also affect daily life. The reminder apps currently in use aim to solve this problem. Margin of error and security vulnerabilities in the management of document validity processes constitute significant risk factors. However, these apps require users to enter information manually. This dependency introduces substantial operational friction, increasing the susceptibility to data entry errors and driving high application abandonment rates. To resolve these issues, the Flutter-based DocGuard mobile application, integrating on-device OCR with a layout-aware heuristic algorithm, was developed to automate and secure data entry, and its performance was evaluated. The developed DocGuard application presents a validated, novel approach that aims to eliminate these risks and prioritizes client-side data privacy. The study’s contribution to the literature consists of three main parts. First, a Layout-Aware Sequence Heuristic (LASH) scoring algorithm capable of reliable date detection across standard structured and semi-structured official documents was designed using the Google ML Kit OCR infrastructure. Second, a privacy architecture prioritizing local processing was established, ensuring closed-loop, strict data confidentiality through standard PBKDF2-HMAC-SHA256 key derivation and AES-256 Client-Side Encryption (CSE) methods optimized for mobile edge devices. The system’s performance was tested using a 5-fold cross-validation method on a balanced dataset consisting of 500 real and open-source documents. The proposed LASH algorithm outperforms standard OCR methods, achieving an exact match accuracy of 94.0% (± 1.2%) for structured documents and 82.0% (± 2.1%) for unstructured documents. Additionally, a statistically significant (p < 0.001) reduction of 70–75% in manual processing time was recorded for users. Although there are some structural limitations in analyzing complex structures and handwriting that require human verification, the findings demonstrate that DocGuard offers a scalable, privacy-preserving digital document lifecycle infrastructure applicable across a wide range of areas, from corporate compliance processes to fintech KYC verifications.

🔖

Cite This Article

Tat, M., Takgil, B. (2026). Automated Expiration Date Extraction from Official Documents: A Real-Time OCR-Based Mobile System. Gazi University Journal of Science Part A: Engineering and Innovation, Volume: 13 Issue: 3. https://doi.org/10.54287/gujsa.1964635

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (36)

  1. Chen, J., & Ran, X. (2019). Deep learning with edge computing: A review. Proceedings of the IEEE, 107(8), 1655-1674. https://doi.org/10.1109/JPROC.2019.2921977
  2. Chen, Y., Yang, Y., Zeng, Z., Bu, H., Yun, C., Wei, B., Liu, K., Liang, C., Lin, D., & Xu, M. (2024). Fine-Grained Layout-Aware Information Extraction for Imaged Documents. In: Proceedings of the 2024 6th International Academic Exchange Conference on Science and Technology Innovation (IAECST) (pp. 504-509), 06-08 December 2024, Guangzhou, China. https://doi.org/10.1109/IAECST64597.2024.11118108
  3. Chouke, A., Jain, V. K., Parmar, J., Shukla, S.S.P., & Verma, A. K. (2025). Identity Verification: A Decentralized KYC Approach Using Blockchain. In: S. Joshi, A. K. Bairwa, M. Radenkovic, & A. Pljonkin (Eds.), Proceedings of the Second International Conference on Cyber Warfare, Security and Space Computing. SpacSec 2024. Communications in Computer and Information Science (Vol. 2195, pp. 252–264). 22–23 February 2024, Jaipur, India. https://doi.org/10.1007/978-3-031-73494-6_19
  4. Dissanayaka, D. M. R. O., Salgado, M. B. D., Udayar, R. S., & Muhammadh, Z. H. (2024). Review paper on the End-to-End encryption technique used in Social media applications. In: V. Sokolov, V. Ustimenko, T. Radivilova, & M. Nazarkevych (Eds.), Proceedings of the Cybersecurity Providing in Information and Telecommunication Systems II (CPITS-II 2024), 26 October 2024, Kyiv, Ukraine. CEUR Workshop Proceedings (Vol. 3826, pp. 1-6).
  5. Dospinescu, O., Grigorcea, G.-L., & Lefter, B.-I. (2025). A Mobile Approach to Food Expiration Date Determination Using OCR and On-Cloud Image Classification. Applied System Innovation, 8(6), 176. https://doi.org/10.3390/asi8060176
  6. Emel, M. H., Terzioğlu, M., & Özkan, R. (2024). Efficient and Accurate Date Extraction from Invoices: A Comprehensive Three-Step Methodology Integrating Custom Object Detection, OCR, and Refined Regular Expressions. Advances in Artificial Intelligence Research, 4(1), 10-17. https://doi.org/10.54569/aair.1401234
  7. Google. (2024). Firebase authentication overview. Firebase Documentation. Accessed May 10, 2026, from https://firebase.google.com/docs/auth
  8. Google. (2025a). Google ML Kit Vision API: Text recognition. Google Developers. Accessed May 10, 2026, from https://developers.google.com/ml-kit/vision/text-recognition
  9. Google. (2025b). Firebase cloud firestore: Flexible, scalable NoSQL cloud database. Firebase Documentation. Accessed May 10, 2026, from https://firebase.google.com/docs/firestore
  10. Google. (2025c). Flutter: Build apps for any screen from a single codebase. Flutter Documentation. Accessed May 10, 2026, from https://flutter.dev/
  11. Guha, D., Saw, S. K., Mukherjee, P., Singh, G., & Satyam. (2025). A Practical Implementation of Zero Trust Architecture for Securing Web-Based Applications. In: S. Mishra, H. K. Tripathy, & J. R. Mohanty (Eds.), Proceedings of the 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) (pp. 97-102), 16-18 May 2025, Bhubaneswar, India. https://doi.org/10.1109/ASSIC64892.2025.11158196
  12. Gupta, I., Singh, A. K., Lee, C.-N., & Buyya, R. (2022). Secure data storage and sharing techniques for data protection in cloud environments: A systematic review, analysis, and future directions. IEEE Access, 10, 71247–71277. https://doi.org/10.1109/ACCESS.2022.3188110
  13. Nisna Haneefa, K., Archana, A., Hariprakash, M., Muhammed Niyas, K., & Sreelakshmi, M. (2026). Offline VQA Chatbot for On-Device Image Understanding: A Privacy-Preserving Edge-AI Approach. In: Y. Lawani (Eds.), Proceedings of the 2026 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1-6) 31 January - 01 February 2026, Bhopal, India. https://doi.org/10.1109/SCEECS68810.2026.11429827
  14. Hwang, W., Yim, J., Park, S., Yang, S., & Seo, M. (2021). Spatial dependency parsing for semi-structured document information extraction. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 330-343. https://aclanthology.org/2021.findings-acl.28/
  15. Jaume, G., Ekenel, H. K., & Thiran, J. P. (2019). Funsd: A dataset for form understanding in noisy scanned documents. In: Proceedings of the 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW) (pp. 1-6), 22-25 September 2019, Sydney, NSW, Australia. https://doi.org/10.1109/ICDARW.2019.10029
  16. Jung, Y., Bao, J. A., Norman, M. P., & Sundar, S. S. (2025). Privacy concerns in mobile technology: Can interactivity reduce friction?. Computers in Human Behavior, 162, 108421. https://doi.org/10.1016/j.chb.2024.108421
  17. Khalfaoui, S., Khalfaoui, H., & Azmani, A. (2025). Microservices-driven automation in full-stack development: Bridging efficiency and innovation with FSMicroGenerator. IEEE Access, 13, 125131-125156. https://doi.org/10.1109/ACCESS.2025.3589285
  18. Li, Y. (2025). Application mode of blockchain technology in user data sovereignty and privacy protection. Journal of Cyber Security and Mobility, 14(5), 1199-1220. https://doi.org/10.13052/jcsm2245-1439.1457
  19. Madake, J., Kalamkar, K., & Katepallewar, P (2025). ExpirySense: Expiry Date Detection and Proactive Alerts. International Journal for Research in Applied Science and Engineering Technology, 13(12), 658–671. https://doi.org/10.22214/ijraset.2025.76176
  20. Mitra, S. (2014). Using UML modeling to facilitate three-tier architecture projects in software engineering courses. ACM Transactions on Computing Education, 14(3), 17. https://doi.org/10.1145/2635831
  21. Monarch, R. M. (2021). Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI. Simon and Schuster.
  22. Mouha, N., & Dworkin, M. (2024). Report on the block cipher modes of operation in the NIST SP 800-38 series (NIST IR 8459). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.IR.8459
  23. Nanavati, J., Patel, S., Patel, U., & Patel, A. (2024). Critical Review and Fine-Tuning Performance of Flutter Applications. In: Proceedings of the 2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) (pp. 838-841), 18-19 January 2024, Lalitpur, Nepal. https://doi.org/10.1109/ICMCSI61536.2024.00131
  24. Ning, M., Wang, Q.-F., Huang, K., & Huang, X. (2021). A Segment-Based Layout Aware Model for Information Extraction on Document Images. In: T. Mantoro, M. Lee, M. A. Ayu, K. W. Wong, & A. N. Hidayanto (Eds.), Proceedings of the 28th International Conference on Neural Information Processing (ICONIP 2021) (pp. 757-765), 8–12 December 2021, Sanur, Bali, Indonesia. https://doi.org/10.1007/978-3-030-92307-5_88
  25. Patel, S., & Bhatt, D. (2020). Abstractive Information Extraction from Scanned Invoices (AIESI) using End-to-end Sequential Approach. https://doi.org/10.48550/arXiv.2009.05728
  26. Peng, H., Bayon, J., Recas, J., & Guijarro, M. (2025). Efficient Expiration Date Recognition in Food Packages for Mobile Applications. Algorithms, 18(5), 286. https://doi.org/10.3390/a18050286
  27. Piyawanno, K., & Nuntawisuttiwong, T. (2024). Hashing Algorithm for Protecting Credential Information Against Channel Interception and Server-Side Data Leakage. In: Proceedings of the 2024 28th International Computer Science and Engineering Conference (ICSEC) (pp. 1-6), 06-08 November 2024, Khon Kaen, Thailand. https://doi.org/10.1109/ICSEC62781.2024.10770628
  28. Pub.dev. (2025a). encrypt: A set of high-level APIs over PointyCastle. Dart Packages. Accessed April 24, 2026, from https://pub.dev/packages/encrypt
  29. Pub.dev. (2025b). Hive: Lightweight and blazing fast key-value database. Dart Packages. Accessed April 20, 2026, from https://pub.dev/packages/hive
  30. Sampathkumar, V., Kotha, R., & Ramaraj, D. K. (2026). Reinforcing digital banking onboarding with generative AI fraud detection. In: R. Paul (Eds.), Proceedings of the 2026 IEEE 16th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0133-0141), 05-07 January 2026, Las Vegas, NV, USA. https://doi.org/10.1109/CCWC67433.2026.11393774
  31. Su, H., Kang, R., & Fan, Y. (2024). Research on a web system data-filling method based on optical character recognition and multi-text similarity. Applied Sciences, 14(3), 1034. https://doi.org/10.3390/app14031034
  32. Sunoj, A., & Sherif, B. V. (2023). Varying Encryption Scheme: An Innovative Approach to Legacy Data Security. In: Proceedings of the 2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS) (pp. 1-5) 16-18 November 2023, Kanjirapally, India. https://doi.org/10.1109/AICERA/ICIS59538.2023.10420200
  33. Watanabe, H., & Uchikoshi, M. (2025). Generating Privacy-Preserving Personalized Advice with Zero-Knowledge Proofs and LLMs. In: Companion Proceedings of the ACM on Web Conference 2025 (WWW’25) (pp. 1385–1389), 28 April - 2 May 2025, Sydney NSW Australia. https://doi.org/10.1145/3701716.3715597
  34. Xie, H., Guo, Y., He, K., Song, Y., & Liu, Y. (2021). Privacy-preserving edge intelligent computing based on federated learning. In: Proceedings of the 2021 7th International Conference on Computer and Communications (ICCC) (pp. 1371-1377), 10-13 December 2021, Chengdu, China. https://doi.org/10.1109/ICCC54389.2021.9674240
  35. Zhang, L., Zhou, L., Guo, G., Sun, Q., & Yang, X.(2025). Research on intelligent detection technology of confidential documents based on OCR and semantic analysis. In: Proceedings of the 2025 5th International Conference on Computer Vision, Application and Algorithm (CVAA) (pp. 25-28), 15-17 August 2025, Ningbo, China. https://doi.org/10.1109/CVAA66438.2025.11193365
  36. Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738-1762. https://doi.org/10.1109/JPROC.2019.2918951