Document Table Digitization via Morphological Image Enhancement
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Solution Overview
Problem
Current document processing techniques often result in inaccurate digitization of table information from documents, especially when tables are unstructured, rotated, tilted, or contain noise, leading to inefficiencies in resource utilization and manual correction processes.
Innovation Solution
A system utilizing machine learning models to detect and extract table information from images, applying morphological operations to enhance the table images when necessary, and converting them into digitized data, thereby reducing errors and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data entry or traditional OCR is used to digitize table information, then the process can be completed, but accuracy deteriorates when tables are unstructured, rotated, tilted, or contain noise
Solution Approach 1:
The system performs preliminary actions by detecting table boundaries, rotation angles, and tilt angles before digitization. Morphological operations are applied in advance to correct rotation and tilt, and noise is reduced beforehand, ensuring accurate digitization without requiring complex real-time processing during extraction
Solution Approach 2:
The patent introduces an intermediary processing stage between image input and digitization. This intermediary layer includes morphological operation modules that act as mediators to correct table image defects (rotation, tilt, noise) before the actual OCR/digitization process, improving accuracy without making the entire system overly complex
2Productivity
If traditional document processing techniques are used, then processing can proceed, but resource utilization deteriorates due to manual correction processes
Solution Approach 1:
The system implements self-service by automatically detecting table issues (rotation, tilt, noise) and applying appropriate morphological operations without human intervention. The automated correction process eliminates the need for manual review and correction, improving productivity while reducing resource consumption associated with human labor
Solution Approach 2:
The patent incorporates feedback mechanisms where the system evaluates table image quality metrics (rotation angle, tilt angle, noise level) and automatically adjusts processing parameters accordingly. This feedback-driven approach optimizes resource utilization by applying corrections only when and where needed, rather than processing all tables uniformly
3Measurement precision
If morphological operations are applied to enhance table images, then extraction accuracy improves, but processing time increases
Solution Approach 1:
The system applies partial morphological operations only when and where needed based on detected table image quality. Instead of applying all possible corrections to every table, the system selectively applies rotation correction, tilt correction, or noise reduction only when corresponding issues are detected, reducing unnecessary processing time while maintaining high extraction accuracy
Data Source
AI summary
A device may receive document image data that includes an image of a document to be digitized. The device may detect, from the document image data, a table of information that is depicted in the image. The device may determine a data extraction score associated with a table image, wherein the data extraction score is associated with using a data conversion technique to convert the table image to digitized table data. The device may perform, based on the data extraction score not satisfying a threshold, a morphological operation on the table image to generate an enhanced table image that corresponds to an enhanced table of information associated with the table of information. The device may process, using the data conversion technique, the enhanced table image to extract the information from the enhanced table. The device may perform an action associated with the extracted information.