Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction (2410.21169v4)

Abstract: Document parsing is essential for converting unstructured and semi-structured documents such as contracts, academic papers, and invoices into structured, machine-readable data. Document parsing reliable structured data from unstructured inputs, providing huge convenience for numerous applications. Especially with recent achievements in LLMs, document parsing plays an indispensable role in both knowledge base construction and training data generation. This survey presents a comprehensive review of the current state of document parsing, covering key methodologies, from modular pipeline systems to end-to-end models driven by large vision-LLMs. Core components such as layout detection, content extraction (including text, tables, and mathematical expressions), and multi-modal data integration are examined in detail. Additionally, this paper discusses the challenges faced by modular document parsing systems and vision-LLMs in handling complex layouts, integrating multiple modules, and recognizing high-density text. It outlines future research directions and emphasizes the importance of developing larger and more diverse datasets.

• The paper comprehensively surveys document parsing methods by comparing modular pipeline systems with evolving visual-language models for structured data extraction.
• It details key components such as layout analysis, OCR, and table recognition, employing deep learning and transformer techniques to enhance accuracy.
• The research highlights challenges in multimodal integration and dataset diversity, paving the way for more efficient and adaptive extraction systems.

Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction

The paper "Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction" offers a comprehensive survey of document parsing, encompassing both modular pipeline systems and advances in large visual-LLMs (VLMs). Document parsing facilitates the conversion of unstructured documents into structured data, crucial for numerous applications, including knowledge management, automated data extraction, and training data generation for machine learning models. The paper dissects various methodologies, evaluates core components involved in parsing, and speculates on future research directions, highlighting the challenges and opportunities within this domain.

Core Methodologies

Two primary methodologies dominate the field of document parsing: modular pipeline systems and end-to-end models grounded in VLMs.

1. Modular Pipeline Systems:

• Layout Analysis: Involves detecting structural elements such as text blocks and tables, employing visual and semantic information integration for accurate extraction. Technologies like CNNs, Transformer models, and graph-based approaches enhance the analysis of complex layouts.
• Optical Character Recognition (OCR): Extracts text using detection and recognition procedures. Advances in deep learning, particularly CNN-based and Transformer architectures, have significantly improved OCR accuracy and efficiency.
• Mathematical Expression Recognition: This task involves detecting and converting mathematical expressions into standardized formats. Yet, challenges remain, particularly in processing diverse presentation styles and contexts.
• Table and Chart Recognition: Requires identifying structures and data within tables and charts. Object detection algorithms and specialized models for these tasks have evolved, yet still face hurdles in handling varying layouts and complex data relationships.

2. End-to-End VLM Models:

While modular systems are robust, VLMs present a shift toward integrated processing of visual and textual information through models like GPT-4, Qwen, and others. These models leverage simultaneous data processing, enhancing document image conversion to structured outputs. Although promising, further development is required to manage dense text and varied document styles effectively.

Key Challenges and Research Directions

The survey identifies critical challenges in current parsing systems, such as the integration of multimodal information and the need for larger, varied datasets to improve model robustness. Future research should focus on:

• Enhancing Multimodal Integration: Efforts to blend visual data with semantic context effectively are vital for improved parsing accuracy, particularly for complex documents featuring dense or nested information.
• Improving Dataset Diversity: Larger, more diverse datasets are necessary to train models capable of handling the wide array of document styles encountered in real-world applications.
• Optimization of Large Models: Refinement of models like Nougat and Fox with attention to efficiency and adaptability is crucial. This includes addressing language diversity and multi-page document processing effectively.

Practical Implications

The advancements in document parsing are poised to impact various applications, from intelligent information retrieval systems and automated archival solutions to enhanced training data pipelines for AI models. As the technology matures, its implementation could lead to more efficient workflows in data-intensive industries, such as finance, healthcare, and academia.

Conclusion

This paper delivers an in-depth exploration of document parsing, presenting both historical perspectives and future trajectories. By dissecting current methodologies and evaluating their efficacy, it offers valuable insights into the progress and potential of this evolving technology. The direction of future research promises significant enhancements in accuracy, application diversity, and computational efficiency, underscoring the importance of continued investigation and innovation in the field of document parsing.