Intelligent Document Processing: The New Frontier of Automation
Keywords:
Intelligent Document Processing (IDP), Robotic Process Automation (RPA), Optical Character Recognition (OCR), Natural Language Processing (NLP), Document Automation, Invoice Processing, Business Process AutomationAbstract
Intelligent Document Processing (IDP) represents a transformative evolution in automation, combining Optical Character Recognition (OCR), machine learning, natural language processing, and business rules to extract, classify, and validate data from complex, unstructured documents. As organizations face increasing volumes of diverse documents and stringent regulatory requirements, IDP offers a scalable and secure solution that transcends traditional Robotic Process Automation (RPA) capabilities. This article explores the technological foundations, practical applications, and benefits of IDP across various industries, highlighting its role in accelerating processes such as invoice handling, customer onboarding, and compliance audits. Additionally, it discusses the challenges of implementation, emerging trends, and the strategic impact of IDP on operational efficiency and digital transformation.
Downloads
References
Olejniczak, K., & Šulc, M. (2022). Text Detection “Forgot About Document” OCR. arXiv.
A comparative study demonstrating that modern 'in the wild' text detection methods outperform traditional OCR systems on structured documents.
Deloitte. (2021). Intelligent Automation in Action: Transforming Processes with IDP and RPA. Deloitte Insights.
Forrester Research. (2022). The Forrester Wave™: Intelligent Document Extraction Platforms, Q1 2022.
Kaur, G., & Chatterjee, P. (2020). Machine Learning-Based Document Classification for Intelligent Workflow Automation. Journal of Artificial Intelligence Research, 69(2), 112–129.
Singh, A., & Sharma, R. (2021). Document Understanding and Information Extraction Using NLP. International Journal of Data Science, 6(3), 45–57.
Lacity, M. C., & Willcocks, L. (2020). Becoming Strategic with Robotic Process Automation. London School of Economics Research Briefing.
Tanwar, S., & Patel, D. (2019). Integrating RPA with AI: Opportunities and Challenges. IEEE Intelligent Systems, 34(6), 75–83.
McKinsey & Company. (2021). Automation at Scale: The Next Digital Frontier.
Sahu, A. & Jain, N. (2022). Intelligent Document Processing in Healthcare: Automating Patient Records. Health Informatics International, 8(1), 22–35.
Cui, L., Xu, Y., Lv, T., & Wei, F. (2021). Document AI: Benchmarks, Models and Applications. arXiv.
Provides a comprehensive survey of document intelligence approaches, including layout analysis, visual information extraction, classification, and pretraining techniques.
Wipro. (2020). Transforming Finance Operations through Intelligent Document Processing. Wipro White Paper.
World Economic Forum. (2022). The Future of Jobs Report: Automation and AI Adoption.
IDC. (2021). Worldwide Intelligent Document Processing Software Forecast, 2021–2025.
Accenture. (2022). From Automation to Augmentation: Leveraging IDP for Digital Transformation.
ISO/IEC 30105-1:2016. (2016). Information Technology—Business Process Outsourcing—Lifecycle Processes.
Harvard Business Review. (2021). The Cognitive Automation Advantage. HBR Digital Article.
Baidya, A. (2021, March). Document Analysis and Classification: A Robotic Process Automation (RPA) and Machine Learning Approach. In 4th International Conference on Information and Computer Technologies (ICICT). IEEE.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.