Form Identification via Image Fingerprinting and Pattern Matching
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Solution Overview
Problem
Document receiving organizations face challenges in automatically identifying and processing diverse printed forms due to variations such as user-added information, noise, and distortions, which renders traditional image comparison techniques ineffective, requiring manual identification.
Innovation Solution
A method and algorithm for candidate identification that processes digital images to emphasize underlying typographical information, corrects for rotational variations, and generates a fingerprint for comparison with stored patterns, enabling automatic classification and matching of forms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image comparison techniques based on pixel and location checking are used, then the process is simple to implement, but the accuracy deteriorates due to noise from user-added information, facsimile markings, coffee stains, ink smudges, and other variations
Solution Approach 1:
The patent segments the form image into multiple feature regions (header region, body region, footer region, etc.) and extracts features from each region separately. This segmentation allows the system to focus on discriminative regions while ignoring noisy areas, thereby improving identification accuracy without requiring complex global processing of the entire image.
Solution Approach 2:
The patent extracts specific feature elements from the form image such as text strings, barcodes, QR codes, and graphical elements. By extracting only the relevant feature elements rather than processing the entire image, the system achieves high accuracy in form identification while keeping the processing complexity manageable through selective feature extraction.
2Productivity
If manual identification and classification of each received form is performed, then the accuracy of form identification is high, but the productivity deteriorates due to the vast quantities of forms that must be processed
Solution Approach 1:
The patent implements a self-service automated system that performs form identification and classification without human intervention. The system automatically extracts features, compares them against a database of known form types, and classifies forms accordingly. This automation enables high processing throughput while maintaining accuracy by using intelligent feature extraction and pattern matching algorithms.
Solution Approach 2:
The patent replaces the mechanical manual identification process with an automated computer-based system that uses image processing, feature extraction, and pattern recognition algorithms. This substitution of mechanical human labor with automated computational processes dramatically increases processing throughput while maintaining or improving identification accuracy through consistent application of classification rules.
3Reliability
If pixel-based image comparison is used, then the device complexity is low, but the reliability deteriorates because user-added information and variations create noise that renders comparison ineffective
Solution Approach 1:
The patent applies local quality analysis by examining specific regions and features of the form image individually rather than treating the entire image uniformly. Different feature extraction methods are applied to different regions (e.g., text extraction in header regions, barcode recognition in specific zones), allowing the system to achieve reliable matching by focusing on high-quality discriminative features while ignoring noisy areas.
Solution Approach 2:
The patent transforms the image data from raw pixel values into meaningful feature parameters such as text strings, geometric properties, and structural characteristics. By changing the parameter representation from pixels to semantic features, the system achieves reliable form matching that is invariant to noise, stains, and minor variations, while the processing complexity remains manageable through efficient feature extraction algorithms.
Data Source
AI summary
Candidate identification utilizing fingerprint identification is disclosed. The method includes receiving a candidate image comprising a plurality of constituent elements arranged in a content pattern, compensating for rotation variation in the content pattern of the received candidate, analyzing each of the plurality of constituent elements comprising the content pattern of the received candidate image to define a bounded area about each of the plurality of constituent elements, building a candidate fingerprint representative of the content pattern wherein the candidate fingerprint is based on the defined bounded area, comparing the candidate fingerprint to a plurality of fingerprints wherein each of the plurality of fingerprints represents one of a plurality of exemplars, identifying one of the plurality of fingerprints that corresponds to the candidate fingerprint, and evaluating the candidate and one or more identified exemplars to determine the best match there between, wherein the identified exemplar corresponds to the one of the plurality of fingerprints.