Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction
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 Large Language Models, 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-language models. 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-language models 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.
Code (0)
등록된 구현이 없습니다.
Tasks
Data IntegrationKnowledge Base ConstructionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval
Document retrieval in real-world scenarios faces significant challenges due to diverse document formats and modalities. Traditional text-based approaches rely on tailored parsing techniques that disregard layout informat…
Knowledge DistillationDocPTBench: Benchmarking End-to-End Photographed Document Parsing and Translation
The advent of Multimodal Large Language Models (MLLMs) has unlocked the potential for end-to-end document parsing and translation. However, prevailing benchmarks such as OmniDocBench and DITrans are dominated by pristine…
NaviDC-OCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
Document parsing aims to transform unstructured documents into structured and machine-readable representations. Recent advances in Vision-Language Models (VLMs) have significantly advanced document parsing. However, exis…
Representation LearningTabRAG: Improving Tabular Document Question Answering for Retrieval Augmented Generation via Structured Representations
Incorporating external knowledge bases in traditional retrieval-augmented generation (RAG) relies on parsing the document, followed by querying a language model with the parsed information via in-context learning. While …
Question AnsweringAn empirical evaluation of AMR parsing for legal documents
Many approaches have been proposed to tackle the problem of Abstract Meaning Representation (AMR) parsing, helps solving various natural language processing issues recently. In our paper, we provide an overview of differ…
Abstract Meaning RepresentationAMR Parsing