Exploring AI-driven approaches for unstructured document analysis and future horizons

Abstract

Abstract In the current industrial landscape, a significant number of sectors are grappling with the challenges posed by unstructured data, which incurs financial losses amounting to millions annually. If harnessed effectively, this data has the potential to substantially boost operational efficiency. Traditional methods for extracting information have their limitations; however, solutions powered by artificial intelligence (AI) could provide a more fitting alternative. There is an evident gap in scholarly research concerning a comprehensive evaluation of AI-driven techniques for the extraction of information from unstructured content. This systematic literature review aims to identify, assess, and deliberate on prospective research directions within the field of unstructured document information extraction. It has been observed that prevailing extraction methods primarily depend on static patterns or rules, often proving inadequate when faced with complex document structures typically encountered in real-world scenarios, such as medical records. Datasets currently available to the public suffer from low quality and are tailored for specific tasks only. This underscores an urgent need for developing new datasets that accurately reflect complex issues encountered in practical settings. The review reveals that AI-based techniques show promise in autonomously extracting information from diverse unstructured documents, encompassing both printed and handwritten text. Challenges arise, however, when dealing with varied document layouts. Proposing a framework through hybrid AI-based approaches, this review envisions processing a high-quality dataset for automatic information extraction from unstructured documents. Additionally, it emphasizes the importance of collaborative efforts between organizations and researchers to address the diverse challenges associated with unstructured data analysis.

Keywords

• Artificial intelligence
• Unstructured document processing
• Printed and handwritten text recognition
• Information extraction
• Optical character recognition
• Semantic segmentation