Intelligent Data Extraction from Image Documents

International Journal of Computer Science and Engineering

© 2024 by SSRG - IJCSE Journal

Volume 11 Issue 11

Year of Publication : 2024

Authors : Dhivya Nagasubramanian

10.14445/23488387/IJCSE-V11I11P104

How to Cite?

Dhivya Nagasubramanian, "Intelligent Data Extraction from Image Documents," SSRG International Journal of Computer Science and Engineering , vol. 11,  no. 11, pp. 25-34, 2024. Crossref, https://doi.org/10.14445/23488387/IJCSE-V11I11P104

Abstract:

Enterprises often possess a vast collection of scanned documents and images with valuable data crucial for organizational growth and success. In the finance industry, for instance, banks manage extensive collateral documents, tax forms, title deeds, and other critical materials, such as check images, syndication records, and flood documentation. Extracting information from these extensive, scanned files typically involves manual data entry, which is time-consuming and susceptible to human error. With advancements in AI, document entity extraction can now be automated in multiple ways. Heuristic methods can be employed for simpler documents where entities consistently appear in predefined spaces. More complex scenarios can leverage AI frameworks, such as Convolutional Neural Networks (CNNs), trained on labeled images to detect regions of interest, producing bounding boxes and confidence scores for the predictions. Generative AI toolkits offer another solution: extracting entities directly from documents or facilitating question-and-answer interactions to retrieve specific information efficiently. This research paper explores how these methodologies can be swiftly adopted based on document complexity, evaluates the advantages and limitations of each approach, and discusses the role of pipeline building in enhancing the accuracy of AI model predictions.

Keywords:

Document intelligence, Document extraction, CNN, Convolutional neural network, Transformer, OCR, Optical character recognition, Encoder-decoder model, Object detection, Layout detection.

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