Document Authentication via Neural Network Segmentation
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Solution Overview
Problem
Current methods for authenticating documents are inadequate in detecting forgery, particularly when documents are tampered with through handwritten amendments, digital overlays, or other manipulations, as they fail to accurately distinguish between genuine and forged content.
Innovation Solution
A method utilizing a neural network-based approach to analyze images of documents, employing YOLO for object detection, OCR for character identification, and recurrent convolutional neural networks to validate characters and generate score values for categories like font, overlay, and steganographic manipulation, with feature vectors classified using artificial neural networks to determine authenticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional document authentication methods are used, then the process is simple and fast, but the detection accuracy of forgery is insufficient
Solution Approach 1:
The patent segments the document into multiple regions of interest (headers, footers, body text, signatures) and analyzes each segment separately using appropriate authentication methods. This allows comprehensive forgery detection while maintaining manageable system complexity through modular processing.
Solution Approach 2:
The patent transitions from traditional 2D image analysis to 3D feature space analysis by extracting multiple features (font characteristics, pixel patterns, metadata, structural properties) and analyzing them in combined feature vectors, enabling more accurate forgery detection.
2Productivity
If manual authentication methods are used, then false positives are reduced, but the processing time and productivity are low
Solution Approach 1:
The system implements feedback mechanisms where authentication results from different document segments and feature analyses are aggregated and cross-validated. The multi-layer neural network receives feedback from various feature extractions and adjusts its classification decisions, maintaining high reliability while enabling automated high-speed processing.
Solution Approach 2:
The patent introduces an intermediary multi-layer neural network that mediates between raw document features and final authentication decisions. This intermediary processes and integrates multiple feature types (font analysis, pixel patterns, metadata) to produce reliable authentication results at high speed.
3Measurement precision
If comprehensive feature analysis is performed on all document characters, then detection precision improves, but the computational resources and time required increase
Solution Approach 1:
The patent divides the document into regions of interest and prioritizes analysis of high-risk segments (headers, footers, signature areas) while applying lighter analysis to low-risk body text. This segmented approach maintains high detection precision for critical areas while reducing overall processing time.
Solution Approach 2:
The system applies comprehensive feature analysis selectively to only those document segments that show anomalies or belong to high-risk categories, rather than performing exhaustive analysis on every character. This partial action approach maintains precision where needed while reducing unnecessary computational overhead.
Data Source
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AI summary
A method of automatically determining whether a document is genuine is provided. The method involves generating an image of the document, pre-processing of the image to obtain at least one segment of the image with an area of interest and dividing the at least one segment into portions containing single characters and/or combinations of characters. A validation of at least two single characters and/or at least two combinations of characters is performed for each of the single character and/or character combinations for at least two different categories. Score values are created for each category for each validated single character and/or character combination. Feature vectors are created for each single character and/or character combination, with the respective score values for each category as components. The method involves classifying the feature vectors to determine whether the single character or character combination to which the feature vector is associated is genuine.