AI-Powered Intelligent Document Processing in Digital Enterprises

  • Authors

    • Dr. Andrew Collins Professor, University of Oxford, UK. Author
    • Dr. Emma Roberts Associate Professor, University of Cambridge, UK. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2018PIIKLM

    Published 10-05-2018

  • Artificial Intelligence, Intelligent Document Processing, Machine Learning, Natural Language Processing, Optical Character Recognition, Digital Transformation, Automation, Deep Learning, Enterprise Systems

    Issue

    Vol. 1 No. 2 (2018)

    Section

    Articles

    How to Cite

    [1]
    A. Collins and E. Roberts, “AI-Powered Intelligent Document Processing in Digital Enterprises”, IJAIDT, vol. 1, no. 2, pp. 01–12, Oct. 2018, doi: 10.67228/30713315/IJAIDT-2018PIIKLM.

  • Abstract

    Intelligent Document Processing (IDP) using Artificial Intelligence (AI) enables organizations to efficiently process and analyze large volumes of unstructured and semi-structured data. Traditional rule-based and manual methods struggle with scalability and complexity, whereas AI-driven IDP leverages machine learning, natural language processing, computer vision, and deep learning to enhance accuracy and decision-making. This paper presents a comprehensive study of AI-based IDP systems before 2019, focusing on their architecture, methodologies, and applications in digital enterprises. It explains how technologies like OCR convert scanned documents into machine-readable formats and how supervised and unsupervised learning improve classification and data extraction. A modular architecture including ingestion, preprocessing, classification, extraction, validation, and storage is discussed. The study highlights improved performance in terms of accuracy, precision, recall, and processing time compared to traditional systems. Applications across banking, healthcare, insurance, and logistics are examined. Finally, key challenges such as data privacy, model interpretability, and system integration are identified, along with future research directions, emphasizing the role of AI-driven automation in digital transformation.

  • References

    [1] Smith, R. (2007). An Overview of the Tesseract OCR Engine. Proceedings of the Ninth International Conference on Document Analysis and Recognition (ICDAR).

    [2] Hull, J. J. (1998). Document Image Skew Detection: Survey and Annotated Bibliography. Document Analysis Systems.

    [3] Zhu, Y., Yao, C., & Bai, X. (2016). Scene Text Detection and Recognition: Recent Advances and Future Trends. Frontiers of Computer Science.

    [4] Appelt, D. E., & Israel, D. J. (1999). Introduction to Information Extraction Technology. AI Communications.

    [5] Cunningham, H. (2002). GATE: A General Architecture for Text Engineering. Computers and the Humanities.

    [6] Sebastiani, F. (2002). Machine Learning in Automated Text Categorization. ACM Computing Surveys.

    [7] Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. European Conference on Machine Learning.

    [8] McCallum, A., & Nigam, K. (1998). A Comparison of Event Models for Naive Bayes Text Classification. AAAI Workshop.

    [9] LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE.

    [10] Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation.

    [11] Graves, A. (2012). Supervised Sequence Labelling with Recurrent Neural Networks. Springer.

    [12] Vaswani, A., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems (NeurIPS).

    [13] Katti, A. R., et al. (2018). Chargrid: Towards Understanding 2D Documents. Proceedings of EMNLP.

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