Form Document Image Matching via Feature Vector Homography
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Solution Overview
Problem
Existing methods for automating the collection of information from form documents require a large number of template images or specific features like horizontal and vertical lines, which are not always available or distinct enough for accurate matching and information extraction.
Innovation Solution
A method that processes a template image to identify processing and alignment regions, generates meaningful vectors, and compares them with query images to determine homography, allowing for efficient matching and information extraction without relying on multiple template images or specific features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fifty sample template images are used to create a template file through summation, then the matching accuracy is improved, but the complexity of the process increases and a large number of template images are required
Solution Approach 1:
The patent applies preliminary action by pre-processing the single template image to extract key features, create a feature vector, and establish a feature database before any query images need to be matched. This eliminates the need to collect and process multiple sample images at runtime, reducing process complexity while maintaining matching accuracy through the pre-established feature representation
Solution Approach 2:
The patent extracts essential features from the template image to create a condensed feature vector that captures the most important characteristics for matching. By taking out only the relevant features rather than using the complete image data or requiring multiple samples, the system achieves accurate matching with reduced complexity and without needing fifty sample images
2Device complexity
If horizontal and vertical lines are used to provide registration data, then the matching process is simplified, but the method fails when such lines are not present or not distinct enough in the template image
Solution Approach 1:
The patent changes the parameter basis for matching from geometric features (horizontal and vertical lines) to feature vector parameters extracted from the image content. This allows the system to adapt to various document types regardless of whether they contain distinct lines, as the feature vector approach works with the actual content characteristics of any image
Solution Approach 2:
The patent creates a universal matching approach using feature vectors that can be applied to any document type or image format. The system is not limited to documents with specific geometric features like horizontal and vertical lines, making it versatile and adaptable to various forms including insurance cards, driver's licenses, and other documents with diverse visual characteristics
3Productivity
If manual re-entry of information is performed, then the information can be stored in a database for efficient retrieval, but the time and labor required for data collection increases
Solution Approach 1:
The patent replaces the mechanical process of manual data entry with an automated image processing system. The system automatically extracts information from the template image, creates feature vectors, and stores them in a database, eliminating the need for manual re-entry while enabling efficient retrieval. This substitution of automated processing for manual operations resolves the contradiction between retrieval efficiency and data collection time
Data Source
AI summary
A template image of a form document is processed to identify alignment regions used to match a query image to the template image and processing regions to identify areas from which information is to be extracted. Processing of the template image includes the identification of a set of meaningful template vectors. A query image is processed to determine meaningful query vectors. The meaningful template vectors are compared with the meaningful query vectors to determine whether the format of the template and query images match. Upon achievement of a match, a homography between the images is determined. In the event a homography threshold has been met, information is extracted from the query image.