Document Template Inference for Accurate Invoice Data Extraction
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Solution Overview
Problem
Conventional methods for extracting information from documents, such as invoices, are tedious, error-prone, and require extensive training data and time, leading to inaccurate results, especially in accounting software.
Innovation Solution
A system that automatically generates and refines document templates based on geometric characteristics, using user feedback to improve accuracy, allowing for real-time extraction of key information without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional OCR technology is used for information extraction, then the extraction process can be automated, but the accuracy and reliability of extracted information deteriorates
Solution Approach 1:
The system segments the document processing task into multiple stages: initial template generation from geometric characteristics, information extraction using the template, and subsequent refinement based on user feedback. This segmentation allows each stage to specialize, with the template providing structured guidance for extraction while leaving room for refinement, thereby maintaining automation while improving accuracy through iterative improvement of the extraction template.
2Measurement precision
If deep learning tools are used for information extraction, then extraction accuracy can be improved, but the requirement for large annotated datasets and lengthy training times increases
Solution Approach 1:
The system performs preliminary action by automatically generating extraction templates from geometric characteristics of documents before the actual information extraction process. These templates pre-define the structure and locations of key information, eliminating the need for lengthy deep learning training. The templates are then refined iteratively based on user feedback, achieving high accuracy without requiring large annotated datasets or extended training periods.
3Measurement precision
If conventional systems use zero-shot prediction models, then best average results can be provided, but the models cannot improve over time
Solution Approach 1:
The system implements feedback by capturing user interactions and corrections during information extraction processes. These feedback signals are used to automatically refine and update extraction templates over time. The templates evolve based on real-world usage patterns and user corrections, enabling the system to continuously improve its accuracy and adapt to new document formats without requiring retraining of deep learning models.
4Measurement precision
If manual entry methods are used for document information, then accuracy can be maintained through human review, but the process becomes tedious and time-consuming
Solution Approach 1:
The system enables self-service by automatically generating extraction templates from geometric characteristics and using them to extract information without requiring manual data entry. The templates are refined based on user feedback, allowing the system to self-improve over time. This approach maintains high accuracy through structured template-based extraction while dramatically increasing productivity by eliminating tedious manual entry operations.
Data Source
AI summary
Various embodiments offer improved functionality for generating and/or refining templates that can be used for automatically extracting information from within an invoice or other document, based on geometric characteristics of the document. An initial template may be automatically generated, and such initial template may then be refined over time based on user feedback, so as to improve reliability and accuracy in information extraction.