Context-Aware Document Augmentation for Heterogeneous Artifact Understanding
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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, particularly in converting printed or handwritten documents into machine-readable formats.
Innovation Solution
A system utilizing OCR extraction, SDF metadata, and large language models (LLMs) to identify and generate natural language descriptions of document artifacts, enhancing digital intelligibility by adding context-aware interpretations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional document processing is used, then processing speed is maintained, but digital intelligibility and contextual understanding are insufficient
Solution Approach 1:
The system performs preliminary extraction of document structure, tables, and images before generating the final augmented document. This preliminary processing enables the LLM to focus on generating context-aware descriptions without delaying the overall processing pipeline, thus improving digital intelligibility while maintaining processing speed.
Solution Approach 2:
A large language model serves as an intermediary between the extracted document components and the final augmented output. The LLM processes structured sections, tables, and images to generate natural language descriptions that enhance digital intelligibility while the automated pipeline maintains processing efficiency.
2Loss of information
If detailed context analysis is performed on all document artifacts, then digital intelligibility is improved, but processing complexity increases
Solution Approach 1:
The document is segmented into distinct structured sections, tables, and images. Each component is processed separately by the LLM, which reduces the complexity of analyzing the entire document at once while ensuring comprehensive contextual analysis of each segment.
Solution Approach 2:
Different processing approaches are applied to different document components based on their specific characteristics. Tables are processed to extract structured data relationships, while images are analyzed for visual context, and text sections are processed for narrative flow. This localized processing optimizes contextual analysis without uniformly increasing complexity across the entire document.
Data Source
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.