Comparative Analysis and Study of Technology Approach to Document Image Understanding from Structured Text Document Image

This paper presents a meticulous comparative analysis of technologies and methodologies employed for document image understanding, with a specific focus on structured text documents. Our study delves into a critical examination of results and methodologies derived from both traditional techniques, such as Optical Character Recognition (OCR) and rule-based systems, and modern advancements, including Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and various other deep learning approaches. Based on evaluation of numerous studies, our assessment seeks to unveil the effectiveness of these approaches and provide a nuanced understanding of their strengths and limitations. F1 score, precision, recall, and accuracy are employed as Performance metrics in the analysis, offering insights into the impact of preprocessing techniques, feature engineering, and data augmentation, and usage of deep learning techniques on overall performance. This research serves as a comprehensive guide for professionals navigating the landscape of document image understanding methodologies, with a specific focus on diverse deep learning paradigms, paving the way for advancements in real-world applications.