Context-Aware Document Augmentation for Heterogeneous Artifacts
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Solution Overview
Problem
Existing document processing technologies fail to capture the contextual relationships and interdependencies of heterogeneous document artifacts, leading to incomplete digital intelligibility and metadata extraction limitations.
Innovation Solution
A system utilizing OCR extraction and structured document format processing, combined with general and image-processing large language models, generates augmented documents by adding contextually aware natural language descriptions to document artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional document processing technologies are used, then document conversion to machine-readable format is achieved, but contextual relationships and interdependencies of heterogeneous document artifacts are not captured
Solution Approach 1:
The document processing system segments the document into multiple heterogeneous artifacts (tables, images, forms, text sections) and processes each artifact type through specialized models. This segmentation allows comprehensive capture of contextual relationships while maintaining manageable processing complexity through modular architecture.
Solution Approach 2:
The system introduces an intermediary layer of natural language descriptions that mediates between the raw document artifacts and the final digital representation. This intermediary layer captures contextual relationships and interdependencies, transforming structured data into semantically enriched representations that preserve document context.
2Loss of information
If comprehensive natural language descriptions are generated for all document artifacts, then digital intelligibility is enhanced, but processing time increases
Solution Approach 1:
The system applies partial action by selectively generating natural language descriptions based on artifact type and complexity. Different artifact types are processed through specialized models with varying levels of detail, ensuring comprehensive coverage where needed while avoiding unnecessary processing time for simpler artifacts.
Solution Approach 2:
The system changes processing parameters dynamically based on artifact characteristics. The level of natural language description generation is adjusted according to artifact type, size, and complexity, optimizing the balance between digital intelligibility enhancement and processing time consumption.
3Measurement precision
If multiple specialized models are used for different artifact types, then processing accuracy is improved, but system complexity increases
Solution Approach 1:
The system implements universality through a unified document processing framework that handles multiple artifact types (tables, images, forms, text) through a common architecture. This multi-functional approach maintains high processing accuracy for each artifact type while reducing overall system complexity through shared components and standardized processing pipelines.
Data Source
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AI summary
A method includes obtaining a document structure from a data repository. The document structure includes multiple structured sections. A table is detected in a first structured section. A table representation of the table is processed by a general large language model (LLM) to generate a natural language description of the table. An image is detected in the first structured section. The image is processed by an image-processing LLM to generate a natural language description of the image. A form is detected in the first structured section. The form is processed by the general LLM to generate a natural language description of the form. The natural language descriptions of the table, image and form are inserted into the first structured section to obtain a modified first structured section. A modified document structure including the modified first structured section is outputted.