Mobile Check Image Validation Using Native ML Frame Selection
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Solution Overview
Problem
Existing mobile operations software programs for remote check deposit face challenges such as slower processing times, crashes, and ineffective validation due to reliance on third-party libraries, and struggle with capturing images that meet specific criteria like brightness, contrast, and rectangular format, especially when dealing with document security features and user authentication.
Innovation Solution
Implementing a mobile application that leverages native image processing software (e.g., Apple VisionKit®) for client-side image processing and validation, using machine-learning models to identify documents and attributes, and validate checks by capturing multiple frames and selecting frames based on preconfigured intervals, aspect ratio, and tilt angle tolerance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If third-party libraries are used for image processing, then functionality is provided, but processing speed decreases and system stability worsens
Solution Approach 1:
The patent extracts the image processing functionality from third-party libraries and implements it using native device capabilities (camera, image processing hardware). This eliminates the performance overhead and stability issues associated with third-party library interactions while maintaining the required functionality.
Solution Approach 2:
The patent replaces the software-based third-party library processing chain with a streamlined native processing pipeline that leverages hardware acceleration. This substitution reduces processing latency and eliminates crashes caused by library interactions.
2Measurement precision
If detailed instructions are provided to users, then validation accuracy improves, but user convenience decreases
Solution Approach 1:
The patent implements real-time feedback mechanisms that monitor image capture quality and provide automated guidance to users. The system analyzes captured frames against validation criteria (brightness, contrast, rectangular format, aspect ratio) and provides contextual feedback, eliminating the need for detailed pre-instructions while maintaining high validation accuracy.
Solution Approach 2:
The system performs self-validation of captured images against predefined criteria, automatically determining whether the image meets requirements. This reduces the burden on users to understand and follow complex instructions, as the system autonomously evaluates and guides the capture process.
3Reliability
If multiple validation criteria are enforced, then document authenticity verification improves, but processing complexity increases
Solution Approach 1:
The patent segments the validation process into distinct modular components: brightness validation, contrast validation, aspect ratio validation, and rectangular format validation. Each component independently evaluates one criterion, making the overall complex validation process manageable and efficient through division of labor.
Solution Approach 2:
The system performs preliminary validation checks during the image capture phase itself, evaluating multiple criteria (brightness, contrast, aspect ratio, rectangular format) before the image is fully processed. This early validation prevents unnecessary processing of non-compliant images and simplifies subsequent authentication steps.
Data Source
AI summary
Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.