Mobile Image Processing Models for Client-Side Document Validation
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Solution Overview
Problem
Existing mobile check deposit solutions face challenges with inconsistent image quality due to low light conditions and shaky hands, difficulty in verifying user identity and document authenticity, and heavy reliance on backend servers for processing, leading to delays and resource consumption.
Innovation Solution
A computing system that performs client-side validation using machine-learning models to enhance image quality, detect document features, and verify user identity, reducing server load by shifting processing to mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If image processing and validation tasks are performed on backend servers, then processing power and resources are available, but server latency increases and resource consumption increases
Solution Approach 1:
The patent segments the image processing workload between client device and backend server. The client device performs initial image capture, quality assessment, and preliminary validation using onboard sensors and processors. Only images that pass client-side validation are transmitted to the backend server for final processing, thereby reducing server latency and resource consumption while maintaining adequate processing power.
2Power
If image processing and validation tasks are performed on backend servers, then comprehensive processing capability is available, but resource consumption increases
Solution Approach 1:
The patent extracts and relocates image quality assessment and preliminary validation functions from the backend server to the client device. By taking out these preprocessing tasks and executing them locally using the device's camera, processor, and sensors, the system reduces the amount of data and processing required on backend servers, thereby decreasing server resource consumption while maintaining comprehensive processing capability through the combined client-server architecture.
3Ease of operation
If remote deposit capture is offered to eliminate travel and staffing requirements, then convenience is improved, but difficulty in verifying user identity and document authenticity increases
Solution Approach 1:
The patent implements preliminary action by performing client-side validation of image quality, document authenticity, and user identity verification before the actual deposit transaction is completed. The system uses the device's camera, sensors, and processing capabilities to assess image quality metrics, detect document security features, and verify user identity in real-time during the capture process, thereby maintaining security and verification accuracy while preserving the convenience of remote deposit capture.
Data Source
AI summary
Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.