Self-adapting Data Acquisition Model for Document Layout Adaptation
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Solution Overview
Problem
Current methods for automatic data acquisition from documents, such as invoices, are error-prone and require manual intervention, especially when dealing with changes in document layout or new formats, leading to inefficiencies and increased false detection rates.
Innovation Solution
A computer-implemented method for continuously self-adapting data acquisition using a model that interprets and corrects raw data from documents, updates the model based on corrected content information, and applies analysis functions to improve detection accuracy and adapt to changing layouts without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual identification and acquisition of data is performed, then data can be acquired from documents with various layouts, but the process is time-consuming and requires human intervention
Solution Approach 1:
The system automatically adapts to new document layouts by self-learning from corrected interpretation data without requiring manual reconfiguration. The model continuously updates itself based on validation feedback, enabling the system to serve itself in adapting to layout changes rather than requiring human operators to manually adjust parameters or rules for each new document format.
2Productivity
If automatic data acquisition is performed using fixed rules, then data can be extracted quickly, but the system is error-prone when document layouts change
Solution Approach 1:
The system transitions from static fixed rules to dynamic adaptive rules. The interpretation model is continuously updated based on validation feedback and corrected data, allowing the system to dynamically adjust its interpretation rules to match changing document layouts while maintaining high detection accuracy and reducing errors.
Solution Approach 2:
The system implements a feedback loop where interpretation data is validated against raw data, corrections are generated when discrepancies are found, and these corrections are used to update the interpretation model. This continuous feedback mechanism enables the system to learn from errors and improve its detection accuracy over time, reducing false detections while maintaining fast automatic processing.
3Measurement precision
If the model is updated frequently to adapt to new layouts, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system performs self-updating of the interpretation model using its own validation feedback without requiring external intervention or complex update mechanisms. The model automatically incorporates corrections from validation processes, enabling continuous improvement of detection accuracy through a relatively simple self-learning mechanism rather than complex external model management.
Data Source
AI summary
The invention relates to a method for continuously self-adapting an arrangement for automatically acquiring data from a document using a model. The method comprises: receiving raw data of a document containing a content item, the raw data including document content information representing the content item; interpreting the received raw data based on the model to generate interpretation data including interpreted content information of the content item of the document; validating and correcting the interpretation data of the document using the raw data to generate correction data of the document, the correction data including corrected content information of the content item of the document; analyzing the interpretation data, the raw data and the correction data of the document to determine whether to update the model or not; and updating the model content information of the model using the corrected content information of the correction data.