Year: 2023
Type: preprint
Abstract: Information extraction (IE) from unstructured documents remains a critical challenge in data processing pipelines. Traditional optical character recognition (OCR) methods and conventional parsing engines demonstrate limited effectiveness when processing large-scale document datasets. This paper presents a comprehensive framework for information extraction that combines Augmented Intelligence (A2I) with computer vision and natural language processing techniques. Our approach addresses the limitations of conventional methods by leveraging deep learning architectures for object detection, particularly for tabular data extraction, and integrating cloud-based services for scalable document processing. The proposed methodology demonstrates improved accuracy and efficiency in extracting structured information from diverse document formats including PDFs, images, and scanned documents. Experimental validation shows significant improvements over traditional OCR-based approaches, particularly in handling complex document layouts and multi-modal content extraction.
Source: arXiv (Cornell University)
Author: Parikh, Aditya
Institution: Vishwakarma Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4389912246