Machine-Learning Image Processing for Check Authenticity Detection
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Solution Overview
Problem
Existing remote deposit capture systems face challenges in verifying the authenticity of checks and users, especially with diverse environmental and hardware conditions, and struggle with validating document security features and user identity, often leading to inefficiencies and security concerns.
Innovation Solution
A computing system that utilizes a machine-learning architecture to process image data from client devices, detecting documents like checks, extracting information, and validating authenticity through object recognition engines, bounding boxes, and dynamic alignment indicators, while generating risk scores and prompts for additional verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If remote deposit capture is implemented to eliminate manual processes and reduce latency, then productivity and convenience are improved, but the ability to verify document authenticity and user identity deteriorates
Solution Approach 1:
The patent replaces manual mechanical inspection processes with automated machine learning-based image analysis systems. The ML models analyze check images to verify authenticity features, detect security elements, and validate document integrity without requiring physical handling or manual examination, thus maintaining verification reliability while enabling remote processing.
Solution Approach 2:
The patent introduces machine learning models as intermediary systems between the user-submitted check images and the deposit processing decision. These models act as mediators that analyze image data, extract relevant features, and provide verification assessments, bridging the gap between remote image submission and reliable authenticity determination.
2Measurement precision
If detailed instructions are provided to users for check deposition to improve verification accuracy, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements self-service through ML-powered automatic image quality assessment and guidance. The system autonomously evaluates captured check images, determines whether they meet quality standards, and provides targeted feedback to users for improvement without requiring them to understand or follow complex verification protocols, thus maintaining accuracy while simplifying operation.
Solution Approach 2:
The patent employs feedback mechanisms where the ML system analyzes uploaded check images and provides real-time guidance to users about image quality issues. The system communicates specific improvements needed (such as lighting, angle, or focus adjustments) enabling users to capture acceptable images without needing to understand detailed verification requirements.
3Reliability
If multiple validation checks are performed on document security features and user identity to improve reliability, then authenticity verification is improved, but device complexity and processing time worsen
Solution Approach 1:
The patent segments the validation process into multiple specialized ML models, each responsible for specific verification tasks such as detecting security features, analyzing document structure, verifying user identity, and assessing image quality. This modular segmentation allows comprehensive validation while maintaining manageable system complexity through divided responsibilities.
Solution Approach 2:
The patent performs preliminary filtering and assessment using ML models to identify obviously fraudulent or non-compliant submissions before applying more complex validation checks. By conducting initial screenings and prioritizing validation efforts based on risk assessment, the system achieves thorough verification without uniformly applying all validation layers to every submission.
Data Source
AI summary
Presented herein are systems and methods for the employment of machine learning models for image processing. A method may include a capture of a video feed including image data of a document at a client device. The client device can provide the video feed to another computing device. The method can include, by the client device or the other computing device object recognition for recognizing a type of document and capturing an image exceeding a quality threshold of the document amongst the frames within the video feed. The method may further include the execution of other image processing operations on the image data to improve the quality of the image or features extracted therefrom. The method may further include anti-fraud detection or scoring operations to determine an amount of risk associated with the image data.