Character-Based Document Extraction for Variable Layouts
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Solution Overview
Problem
Conventional document processing techniques struggle to extract unstructured content from documents with layout variability, leading to error-prone and resource-intensive efforts.
Innovation Solution
Implement character-based representation learning using artificial intelligence techniques, including character embedding, graph-related techniques, and statistical modeling to identify and classify document content, enabling efficient extraction of structured information from unstructured documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional document processing techniques are used to extract unstructured content, then extraction can be performed on documents, but the process becomes error-prone and resource-intensive due to layout variability
Solution Approach 1:
The patent segments the document processing task into distinct stages: layout analysis to identify structural elements, content extraction to retrieve text from identified regions, and information assembly to organize extracted data. This segmentation allows each stage to specialize in specific operations, improving reliability while managing complexity through modular processing steps.
Solution Approach 2:
The patent introduces an intermediary layout analysis stage that acts as a mediator between the raw unstructured document and the final extracted information. This intermediary step identifies and structures layout elements (headers, paragraphs, lists, tables) before content extraction, thereby reducing errors from layout variability and improving overall extraction reliability without directly increasing final system complexity.
2Adaptability or versatility
If conventional document processing techniques are used to handle layout variability, then all document formats can be processed, but the resources and time required increase significantly
Solution Approach 1:
The patent implements dynamic processing by first analyzing the document's layout structure and then adapting the extraction strategy to the specific document type and format encountered. The system dynamically identifies layout patterns (such as headers, paragraphs, lists, tables) and adjusts extraction methods accordingly, allowing high adaptability to various layouts while maintaining efficiency by avoiding uniform complex processing for all documents.
Solution Approach 2:
The patent changes processing parameters based on detected layout characteristics. Different extraction techniques and confidence thresholds are applied depending on the identified document structure and layout complexity. This parameter adaptation allows the system to handle diverse layouts efficiently by optimizing processing intensity and methods to match actual document characteristics rather than applying maximum resources uniformly.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for character-based representation learning for information extraction using artificial intelligence techniques are provided herein. An example computer-implemented method includes identifying, from unstructured documents, words and corresponding document position information using artificial intelligence-based text extraction techniques; generating an intermediate output by implementing at least one character embedding with respect to the unstructured documents using at least one artificial intelligence-based encoder; determining structure-related information for at least a portion of the unstructured documents using one or more artificial intelligence-based graph-related techniques; generating a character-based representation of at least a portion of the unstructured documents using at least one artificial intelligence-based decoder; classifying one or more portions of the character-based representation using one or more artificial intelligence-based statistical modeling techniques; and performing one or more automated actions based on the classifying.