Document Text Extraction Models for Unknown Layouts
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Solution Overview
Problem
Existing document extraction systems face challenges in handling unknown document layouts and require significant setup effort for new document types, leading to increased costs and time-to-solution, especially when used by multiple clients with varying document formats.
Innovation Solution
A method that attributes learning models to extraction entities rather than document types, allowing for independent improvement and sharing of models across users, enabling efficient configuration of new document types and reducing redundancy through a swarm learning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If learning models are trained universally for each document type, then extraction accuracy improves, but setup costs and time-to-solution increase significantly for multiple clients
Solution Approach 1:
The patent applies universality by training a single learning model on aggregated data from multiple clients to serve all clients simultaneously. Instead of creating separate client-specific models, the system uses a universal model that benefits from diverse training data while reducing setup time and costs for individual clients.
Solution Approach 2:
The patent combines training data from multiple clients into a unified dataset for model training. By merging data sources and training a single model on this aggregated data, the system achieves economies of scale and eliminates redundant training efforts across different clients.
2Measurement precision
If client-specific learning is implemented, then extraction accuracy for each client improves, but system setup becomes more expensive and time-consuming
Solution Approach 1:
The patent eliminates client-specific learning by implementing a universal learning model that serves all clients. This reduces system complexity by removing the need to manage, maintain, and update multiple separate models for different clients.
Solution Approach 2:
The patent segments the learning process by separating the training phase (aggregated across all clients) from the inference phase (applied individually to each client's documents). This segmentation allows a single trained model to be efficiently deployed across multiple clients without requiring client-specific model instances.
3Measurement precision
If templates are created for each document layout, then extraction accuracy improves, but the system becomes complex and cannot handle unknown layouts
Solution Approach 1:
The patent replaces the mechanical template-based system with a learning-based system. Instead of manually creating and maintaining templates for each document layout, the system uses machine learning models that automatically adapt to different layouts through training data, eliminating the need for manual template configuration.
Solution Approach 2:
The patent introduces dynamics by enabling the system to adapt to new document layouts automatically through continuous learning from aggregated data. The model can dynamically adjust to unknown layouts without requiring manual template creation, making the system flexible and versatile.
4Measurement precision
If free-form recognition rules are created for each document variant, then extraction accuracy improves, but rule creation becomes time-consuming and error-prone
Solution Approach 1:
The patent replaces manual rule creation with automated learning. Instead of requiring experts to hand-craft extraction rules for each document variant, the system uses machine learning models that automatically learn extraction patterns from training data, significantly reducing setup time and eliminating human error.
Solution Approach 2:
The system performs self-service by automatically learning and adapting to document formats through aggregated training data. The learning model autonomously identifies patterns and improves extraction accuracy without requiring manual rule configuration or expert intervention for each new document type.
Data Source
AI summary
Method for automatic extraction of information from a document file, wherein said document file contains at least one extraction entity in form of a value to be found, wherein a list of different recognizable document types is available, at least one extraction entity is configured for each document type, each extraction entity is assigned a specific extraction model and each extraction model is independent of said document type, with the following steps: Recognition of the document type of said document file using a classification model and Extraction of the information contained an extraction entity contained in the document file using the specific extraction model assigned to the extraction entity which is configured for the recognized document type, wherein at least two different document types are configured to have at least one identical extraction entity with the same assigned extraction model.