Cracking Document Parsing:Technologies and Datasets for Structured Information Extraction

In the digital age, data structured processing is vital. Document parsing, a magic key for unstructured and semi-structured docs like contracts, papers, and invoices, converts them into machine-readable data, benefiting many apps. With large language model progress, it’s key in knowledge base and training data creation.

This blog will comprehensively review its current state, from modular pipeline to end-to-end vision-language models, analyzing layout detection, content extraction, and multi-modal integration. We’ll also face challenges in complex layout, module integration, and high-density text recognition, explore dataset importance, and look to future trends.

Let’s review the relevant technologies through a survey article next. The article introduces the traditional pipeline-based document parsing technologies, end-to-end multi-modal document parsing technologies and related datasets.

Figure 1: Overview of Document Parsing Methodology.

Technologies

Figure 2: Two Methodology of Document Parsing

Pipeline Parsing Technology Based on Layout Analysis

1.Layout Analysis

Figure 3: Overview of the DLA Algorithm

Layout detection identifies the structural elements of a document, such as text blocks, paragraphs, headings, images, tables, and mathematical expressions, as well as their spatial coordinates and reading order. Among them, the detection of mathematical expressions, especially inline mathematical expressions, usually requires a separate detection model for processing.

Related datasets:

2.Content Extraction

• Text Extraction: This process utilizes Optical Character Recognition (OCR) technology for extraction.

Figure 4: Overview of the OCR Algorithm

Related datasets:

• Mathematical Expression Extraction: Detect the mathematical symbols and structures within the document area and convert them into standard formats, such as LaTeX or MathML.

Figure 5: Overview of the Mathematical Expression Detection and Recognition

Related datasets:

• Table Data and Structure Extraction: Table recognition involves detecting and interpreting the table structure by identifying the layout of cells and the relationships between rows and columns in the document image. The extracted table data is usually combined with the results of Optical Character Recognition (OCR) and converted into formats like LaTeX for further use.

Figure 6: Overview of the Table Detection and Recognition

Related datasets:

• Chart Recognition: This step focuses on identifying different types of charts and extracting the underlying data and its structural relationships. The visual information in the charts is converted into raw data tables or structured formats, such as JSON.

Figure 7: Overview of the Chart-related Tasks in Document

Related datasets:

End-to-End Multimodal Document Parsing Technologies

Traditional modular document parsing systems perform well in specific domains, but their architectures usually lead to insufficient joint optimization and limit the generalization ability among different document types. In recent years, the advancements of Vision-Language Models (VLMs) have provided promising alternatives in this field. These models, such as GPT-4, Qwen, LLaMA and InternVL, are capable of processing visual and text data simultaneously, facilitating the end-to-end transformation from document images to structured outputs.

In response to specific challenges in document images, such as dense text, complex layouts and high variability of visual elements, some large models specifically designed have emerged, like Nougat, Fox and GOT. These models demonstrate stronger adaptability and accuracy when dealing with complex document structures.

Summary

Currently, the implemented solutions for document intelligent parsing are still in the form of the pipeline approach. The end-to-end solutions are still some way from being implemented due to factors such as limited resources and speed.

References

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

📖See Also

• Demystifying-Unstructured-Data-Analysis-A-Complete-Guide
• Cracking-Document-Parsing-Technologies-and-Datasets-for-Structured-Information-Extraction
• Comparison-of-API-Services-Graphlit-LlamaParse-UndatasIO-etc-for-PDF-Extraction-to-Markdown
• Comparing-Top-3-Python-PDF-Parsing-Libraries-A-Comprehensive-Guide
• Assessment-Unveiled-The-True-Capabilities-of-Fireworks-AI
• Assessment-of-Microsofts-Markitdown-series2-Parse-PDF-files
• Assessment-of-MicrosoftsMarkitdown-series1-Parse-PDF-Tables-from-simple-to-complex
• AI-Document-Parsing-and-Vectorization-Technologies-Lead-the-RAG-Revolution