Dynamic OCR Tuning for Outlier Data Fields
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Solution Overview
Problem
The complexity of optical character recognition (OCR) processes increases significantly as the volume and variety of documents grow, and manual entry of data fields in outlier positions is time-consuming and prone to user error, necessitating a dynamic tuning solution for OCR processes to efficiently extract data from images of documents.
Innovation Solution
A system dynamically tunes OCR processes by receiving images of documents, identifying missing data fields, displaying them to users for input of updated coordinate areas, and applying data field-specific OCR to extract values, which are then stored for future processing, reducing manual intervention and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a general OCR process is applied to all document images, then it can handle various document types, but the processing power and time required increases significantly
Solution Approach 1:
The patent segments the document processing by dividing it into two stages: first applying a general OCR process to identify document type and locate data fields, then applying data field-specific OCR processes only to identified regions. This segmentation allows the system to maintain versatility for handling various document types while improving processing efficiency by limiting intensive OCR operations to specific areas rather than processing entire documents uniformly.
2Productivity
If data field-specific OCR processes are applied only to identified regions, then processing efficiency improves, but the complexity of the OCR process system increases significantly
Solution Approach 1:
The patent applies preliminary action by first using a general OCR process to identify document types and locate data fields before applying data field-specific OCR processes. This preliminary identification step simplifies the overall system complexity because the specific OCR processes are only activated when and where needed, rather than requiring all possible OCR processes to be simultaneously available and configured.
Solution Approach 2:
The system dynamically adjusts the OCR processing approach based on the identified document type and data field locations. Rather than using a static, one-size-fits-all OCR configuration, the system adapts by selecting and applying appropriate data field-specific OCR processes only to relevant regions, thereby managing complexity through dynamic decision-making rather than pre-configuring all possible scenarios.
3Measurement precision
If manual entry is used for outlier data fields in unexpected positions, then extraction accuracy improves, but time consumption and user error increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically identify and correct outlier data field positions through the dynamic tuning mechanism. When unusual document layouts are encountered, the system autonomously adjusts the expected coordinate regions and applies appropriate OCR processes without requiring manual intervention, thereby maintaining high extraction accuracy while eliminating the time loss and error risks associated with manual entry.
Solution Approach 2:
The system uses feedback from the initial general OCR analysis to dynamically adjust subsequent processing. When data fields are found in unexpected positions, the system receives feedback about these outliers and automatically tunes the expected coordinate regions, creating a closed-loop system that improves accuracy for outlier cases without requiring manual correction while reducing the time associated with manual entry processes.
Data Source
AI summary
Embodiments of the present invention provide a system for dynamically tuning optical character recognition (OCR) processes. The system receives or captures an image of a resource document and uses a general or default OCR process to identify a source of the document and values of multiple data fields in the image of the document. When the system determines that a data field is missing or cannot be extracted, it causes a computing device to display the image of the resource document and requests user input of a coordinate area of the missing data field from an associated specialist. Once the user input is received, the system applies a data field-specific OCR process on the coordinate area of the missing data field to extract the value of the data field. This value of the missing data field can be transmitted to a processing system for further processing.