Document Image Capture With ML Verification for Remote Check Deposit
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Solution Overview
Problem
Existing remote deposit capture systems face challenges in verifying the authenticity of checks and users, particularly in diverse environmental and hardware conditions, and struggle with validating document security features and user identity, which are not adequately addressed by existing technologies.
Innovation Solution
A system that uses machine-learning architectures to detect and process document imagery from mobile devices, using object recognition engines to extract and validate document information, and generate prompts for alignment indicators to improve image data, including bounding boxes for document imagery using object recognition bounding boxes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If remote deposit capture is implemented to eliminate manual processes and travel, then productivity and convenience are improved, but the ability to verify document authenticity and user identity deteriorates
Solution Approach 1:
The system performs preliminary actions by capturing a video feed of the document before final processing. This video capture allows the system to analyze multiple frames and detect security features, anomalies, and document characteristics in advance, enabling authenticity verification to occur before the deposit is finalized, thus resolving the contradiction between remote processing efficiency and verification reliability
Solution Approach 2:
The system implements feedback mechanisms by analyzing video feed frames and providing risk scores, anomaly detections, and verification results. This feedback loop allows continuous assessment of document authenticity during the remote deposit process, ensuring that productivity gains do not compromise verification reliability through real-time monitoring and validation
2Ease of operation
If electronic facsimiles are accepted from various devices and environments, then ease of operation and accessibility are improved, but measurement precision and validation accuracy deteriorate
Solution Approach 1:
The system captures a video feed of the document as a preliminary action before processing. This video capture occurs at the user's device in their environment, maintaining ease of operation, while the subsequent frame-by-frame analysis extracts precise measurements and validates document features, resolving the contradiction between accessibility and validation accuracy
Solution Approach 2:
The system transitions from static image analysis to temporal video frame analysis. By examining multiple frames over time, the system can detect security features, validate document characteristics, and measure precision attributes that are not visible in single static images, thereby maintaining ease of operation while improving measurement precision through the temporal dimension
3Measurement precision
If video feed analysis is performed to detect anomalies and validate authenticity, then measurement precision and security are improved, but processing time and computational complexity increase
Solution Approach 1:
The system applies partial action by analyzing only the necessary video frames required for validation rather than processing every frame in detail. It performs preliminary anomaly detection on selected frames, achieving sufficient measurement precision for security validation while minimizing processing time by avoiding exhaustive analysis of all video data
Solution Approach 2:
The system skips unnecessary processing steps by directly analyzing video frames for critical security features and anomalies rather than performing sequential preprocessing. This allows rapid extraction of essential validation information, improving anomaly detection accuracy while reducing overall processing time by rushing through to the critical validation steps
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.