Leveraging OCR and AI Tools: A Comparative Guide to Enhancing Data Processing and Decision-Making Efficiency in the Digital Age

İlker Ali; Aybeyan Selim; Fehmi Skender

doi:10.31127/tuje.1796566

Leveraging OCR and AI Tools: A Comparative Guide to Enhancing Data Processing and Decision-Making Efficiency in the Digital Age

Abstract

In this era of digital revolution, efficient processing and leveraging of data are critical to organizational success. The paper is a comparative analysis of Optical Character Recognition (OCR) software and Artificial Intelligence (AI) tools, their strengths, weaknesses, and best fit applications. OCR, which can turn printed or hand written text into machine-readable form, excels in document processing and automation but falls short with low quality images and complex layouts. AI tools, however, offer unparalleled flexibility in advanced data analysis, predictive modeling, and decision-making support but at increased resource utilization and ethical concerns. The study also explores scenarios under which the intersection of OCR and AI can offer maximized outcomes, such as in healthcare, finance, and marketing. By contrasting the technologies comparatively, the paper presents real world recommendations to practitioners who intend to enhance efficiency as well as decision-making in a dynamic technological landscape. This review synthesizes 32 sources and 3 sectoral case studies; reported performance spans 98–99.5% printed text accuracy and 95–99% EMR on key fields.

Keywords

References

Aydin, Ö. (2021). Classification of documents extracted from images with optical character recognition methods. Computer Science, 6(2), 46–55.
Smith, R. (2007). An overview of the Tesseract OCR engine. In Proceedings of the Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), 629–633. Curitiba, Brazil. IEEE. https://doi.org/10.1109/ICDAR.2007.4376991.
Xiong, S., Xiaoyang, C., & Huitao, Z. (2023). Deep learning-based multifunctional end-to-end model for optical character classification and denoising. Journal of Computational Methods in Engineering Applications, 3(1), 1–13.
Wang, S., Huang, L., Gao, A., Ge, J., Zhang, T., Feng, H., Satyarth, I., Li, M., Zhang, H., & Ng, V. (2023). Machine/deep learning for software engineering: A systematic literature review. IEEE Transactions on Software Engineering, 49(3), 1188–1231.
Badmus, O., Rajput, S., Arogundade, J., & Williams, M. (2024). AI-driven business analytics and decision making. World Journal of Advanced Research and Reviews, 24, 616–633.
Ling, X., Gao, M., & Wang, D. (2020). Intelligent document processing based on RPA and machine learning. In Proceedings of the 2020 Chinese Automation Congress (CAC), 1349–1353. Shanghai, China: IEEE. https://doi.org/10.1109/CAC51589.2020.9326579.
Hao, L., Huijin, W., & Jieyun, B. (2022). DeepSSR: A deep learning system for structured recognition of text images from unstructured paper-based medical reports. Annals of Translational Medicine, 10(13).
Ziwei, Y., Xinwei, C., Zuyan, C., & Shuai, L. (2023). Artificial intelligence in accounting and finance: Challenges and opportunities. IEEE Access, 11, 129100–129123.

Kırksekiz, A., Yıldız, M., Kıyıcı, M., & Yıldız, M. (2024). Adaptation of artificial intelligence literacy scale: Latent profile analysis. Sakarya University Journal of Education, 14(3), 581–596.
Selim, A. (2024). Teoriden pratiğe veri madenciliği. Gostivar: International Vision University. ISBN: 608-67100-1-9
Aydin, Ö., & Karaarslan, E. (2022). OpenAI ChatGPT generated literature review: Digital twin in healthcare. In Proceedings of Emerging Computer Technologies 2, 22–31. Izmir, Turkey: Izmir Akademi Derneği.
Aydin, Ö., & Karaarslan, E. (2023). Is ChatGPT leading generative AI? What is beyond expectations? Academic Platform Journal of Engineering and Smart Systems, 11(3), 118–134.
Selim, A., Ali, I., Saračević, M. H., & Ristevski, B. (2024, August). Application of the digital twin model in higher education. Multimedia Tools and Applications, 83(28).
Ali, I., Ristevski, B., & Jolevski, I. (2022). Pair programming in primary education – A Macedonian case study. In Proceedings of the 12th International Conference on Applied Internet and Information Technologies (AIIT2022). Zrenjanin, Serbia.
Skender, F., & Ali, I. (2022). Big data in health and the importance of data visualization tools. Journal of Intelligent Systems with Applications, 5(1), 33–37.
Yiğit, G. (2025). A comparative study of deep learning approaches for human action recognition. Turkish Journal of Engineering, 9(2), 281–289.
Alan, A. Y., Karaarslan, E., & Aydın, Ö. (2025). Improving LLM reliability with RAG in religious question-answering: MufassirQAS. Turkish Journal of Engineering, 9(3), 544–559.
Özkurt, C. (2024). Interpretable AI analysis of chaos systems distribution in time series data from industrial robotics. Turkish Journal of Engineering, 8(4), 656–665.
Christian, R., Uwe, S., Christoph, W., & Frank, P. (2018). Improving OCR accuracy on early printed books by combining pretraining, voting, and active learning. Journal for Language Technology and Computational Linguistics, 33(1), 3–24.
Rani, S., Rehman, A. U., Yousaf, B., Rauf, H. T., Nasr, E. A., & Kadry, S. (2022). Recognition of Handwritten Medical Prescription Using Signature Verification Techniques. Computational and mathematical methods in medicine, 2022, 9297548. https://doi.org/10.1155/2022/9297548
Peng, X., & Wang, C. (2020). Building super-resolution image generator for OCR accuracy improvement. In M. Worring, et al. (Eds.), Document analysis systems: 14th IAPR international workshop, DAS 2020, Wuhan, China, July 26–29, 2020, proceedings, 145–160. Springer. https://doi.org/10.1007/978-3-030-57058-3_11
Cao, R., & Luo, P. (2021). Extracting zero-shot structured information from form-like documents: Pretraining with keys and triggers. Proceedings of the AAAI Conference on Artificial Intelligence, 35(14), 12612–12620. https://doi.org/10.1609/aaai.v35i14.17494
Businessware Technologies. (2024, September 20). AWS Textract vs Google, Azure, and GPT-4o: Invoice extraction benchmark. Retrieved September 20, 2025, from https://www.businesswaretech.com/blog/research-best-ai-services-for-automatic-invoice-processing
Google. (2025, May 5). Document AI pricing. Google Cloud. Retrieved September 20, 2025, from https://cloud.google.com/document-ai/pricing
Microsoft. (2025, August 10). Azure AI document intelligence pricing. Microsoft Azure. Retrieved September 20, 2025, from https://azure.microsoft.com/en-us/pricing/details/ai-document-intelligence/
Amazon Web Services. (2025, July 20). Amazon Textract pricing. AWS. Retrieved September 20, 2025, from https://aws.amazon.com/textract/pricing/
Business Insider. (2025, May 1). A healthcare giant is using AI to sift through millions of transactions. It’s saved employees 15,000 hours a month. Business Insider. Retrieved September 21, 2025, from https://www.businessinsider.com/omega-healthcare-uipath-ai-document-processing-health-transactions-2025-6
Ocrolus. (2025, February 1). Neighborhood Loans reduces processing time by 90% with Ocrolus AI. Ocrolus. Retrieved September 2, 2025, from https://www.ocrolus.com/customer-stories/neighborhood-loans-ai-lending-processing/
Weiss, D. C. (2017, March 2). JPMorgan Chase uses tech to save 360,000 hours of annual work by lawyers and loan officers. ABA Journal. Retrieved August 27, 2025, from https://www.abajournal.com/news/article/jpmorgan_chase_uses_tech_to_save_360000_hours_of_annual_work_by_lawyers_and
Selim, A., & Ali, I. (2022). Dijital Okuryazarlık Becerilerinin Kuzey Makedonya'da YüksekögretimÜzerindeki Etkisi. 6. In International Congress of Economics & Business, 81-91.
Mondal, H., & Mondal, S. (2023). Ethical and social issues related to AI in healthcare. Methods in Microbiology, 55, 247–281.
Devineni, S. K. (2024). AI in data privacy and security. International Journal of Artificial Intelligence and Machine Learning, 3, 35–49.

Details

Primary Language

English

Subjects

Requirements Engineering, Automated Software Engineering

Journal Section

Research Article

Authors

İlker Ali
0000-0002-2111-415X
Macedonia

Aybeyan Selim ^*
0000-0001-8285-2175
Macedonia

Fehmi Skender
0000-0001-6046-5772
Macedonia

Early Pub Date

November 4, 2025

Publication Date

December 16, 2025

Submission Date

October 3, 2025

Acceptance Date

November 3, 2025

Published in Issue

Year 2026 Volume: 10 Number: 1

DOI

https://doi.org/10.31127/tuje.1796566

IZ

https://izlik.org/JA32MH94JY

Cite

RIS / Bibtex

APA

Ali, İ., Selim, A., & Skender, F. (2025). Leveraging OCR and AI Tools: A Comparative Guide to Enhancing Data Processing and Decision-Making Efficiency in the Digital Age. Turkish Journal of Engineering, 10(1), 129-142. https://doi.org/10.31127/tuje.1796566

AMA

1.Ali İ, Selim A, Skender F. Leveraging OCR and AI Tools: A Comparative Guide to Enhancing Data Processing and Decision-Making Efficiency in the Digital Age. TUJE. 2025;10(1):129-142. doi:10.31127/tuje.1796566

Chicago

Ali, İlker, Aybeyan Selim, and Fehmi Skender. 2025. “Leveraging OCR and AI Tools: A Comparative Guide to Enhancing Data Processing and Decision-Making Efficiency in the Digital Age”. Turkish Journal of Engineering 10 (1): 129-42. https://doi.org/10.31127/tuje.1796566.

EndNote

Ali İ, Selim A, Skender F (December 1, 2025) Leveraging OCR and AI Tools: A Comparative Guide to Enhancing Data Processing and Decision-Making Efficiency in the Digital Age. Turkish Journal of Engineering 10 1 129–142.

IEEE

[1]İ. Ali, A. Selim, and F. Skender, “Leveraging OCR and AI Tools: A Comparative Guide to Enhancing Data Processing and Decision-Making Efficiency in the Digital Age”, TUJE, vol. 10, no. 1, pp. 129–142, Dec. 2025, doi: 10.31127/tuje.1796566.

ISNAD

Ali, İlker - Selim, Aybeyan - Skender, Fehmi. “Leveraging OCR and AI Tools: A Comparative Guide to Enhancing Data Processing and Decision-Making Efficiency in the Digital Age”. Turkish Journal of Engineering 10/1 (December 1, 2025): 129-142. https://doi.org/10.31127/tuje.1796566.

JAMA

1.Ali İ, Selim A, Skender F. Leveraging OCR and AI Tools: A Comparative Guide to Enhancing Data Processing and Decision-Making Efficiency in the Digital Age. TUJE. 2025;10:129–142.

MLA

Ali, İlker, et al. “Leveraging OCR and AI Tools: A Comparative Guide to Enhancing Data Processing and Decision-Making Efficiency in the Digital Age”. Turkish Journal of Engineering, vol. 10, no. 1, Dec. 2025, pp. 129-42, doi:10.31127/tuje.1796566.

Vancouver

1.İlker Ali, Aybeyan Selim, Fehmi Skender. Leveraging OCR and AI Tools: A Comparative Guide to Enhancing Data Processing and Decision-Making Efficiency in the Digital Age. TUJE. 2025 Dec. 1;10(1):129-42. doi:10.31127/tuje.1796566

Cited By

Enhancing Agile Manufacturing Efficiency: A Markov-Based Approach to Flexibility Optimization

Turkish Journal of Engineering

https://doi.org/10.31127/tuje.1813047

Generative AI in Academic Writing: A Comparison of DeepSeek, Qwen, ChatGPT, Gemini, Llama, Mistral, and Gemma

Turkish Journal of Engineering

https://doi.org/10.31127/tuje.1838765