Document Intelligence: Techniques for Extracting Structured Information from Documents

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In previous articles, I have shared many document intelligent parsing-related technologies.

Previous articles are organized in the collection “Document Intelligence” for your reference.

Below, we will review the related technologies through a comprehensive article. This article introduces traditional pipeline document parsing technology, end-to-end multimodal document parsing technology, and relevant datasets.

Overview of Document Parsing Methods

Technical Methods

Two methodologies of document parsing: Traditional pipeline document parsing technology, End-to-end multimodal document parsing technology.

Layout Analysis-based Pipeline Parsing Technology

Layout Analysis

Layout detection identifies structural elements of the 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, is usually handled by a separate detection model.

Relevant Datasets:

Content Extraction

Text Extraction

This process extracts text using optical character recognition (OCR) technology.

Relevant Datasets

Mathematical Expression Extraction

Detects mathematical symbols and structures within document regions and converts them to a standard format such as LaTeX or MathML.

Formula Recognition and Parsing

Relevant 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. Extracted tabular data is usually combined with OCR results and converted to formats like LaTeX for further use.

Table Parsing

Relevant Datasets:

Chart Recognition

This step focuses on recognizing different types of charts and extracting the underlying data and their structural relationships. Visual information in charts is converted to raw data tables or structured formats like JSON.

Chart Parsing

Relevant Datasets:

Relation Integration

This step is based on the results of the previous two steps (coordinates, bbox, etc.). It is usually a rule-based system or a specialized reading order model “【Document Intelligence】Document model that conforms to human reading order - LayoutReader and non-official weight open source” to maintain the logical relationship of the content.

End-to-end Multimodal Document Parsing Technology

Traditional modular document parsing systems excel in specific domains, but their architecture often leads to suboptimal joint optimization, limiting generalization ability across different document types. In recent years, advancements in vision-language models (VLMs) have provided a promising alternative in this field. These models, such as GPT-4, Qwen, LLaMA and InternVL, can process both visual and textual data simultaneously, facilitating end-to-end conversion from document images to structured output.

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

Summary

Currently, the implemented document intelligent parsing solutions are still in the form of pipelines. End-to-end solutions are still some distance away from implementation due to limitations in resources, speed, etc.

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