Machine Learning Image Processing for Remote Deposit Capture
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Solution Overview
Problem
Existing remote deposit capture systems face challenges in accurately extracting information from diverse image data environments and validating user identity and document authenticity, particularly with varying hardware configurations and environmental conditions.
Innovation Solution
A computing system that utilizes a client device to stream video feeds to a server, employing a machine-learning architecture for object recognition to detect and extract information from checks, including quality selection and dynamic alignment indicators, while generating risk scores to validate authenticity and identity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If remote deposit capture is implemented to reduce travel and staffing requirements, then convenience and time savings are improved, but verification of user identity and document authenticity becomes more challenging
Solution Approach 1:
The system provides real-time feedback to users during the capture process, guiding them to properly position documents and indicating when capture requirements are met. This feedback mechanism ensures verification quality while maintaining remote convenience.
Solution Approach 2:
The system introduces an intermediary verification process using machine learning models that analyze captured images to validate document authenticity and user identity, bridging the gap between remote convenience and reliable verification.
2Reliability
If detailed instructions are provided to users to ensure proper document capture, then verification quality is improved, but user convenience is reduced
Solution Approach 1:
The system enables users to self-verify their capture quality through real-time feedback and guidance, automatically adjusting capture parameters and providing corrective instructions only when necessary, thereby maintaining convenience while ensuring quality.
Solution Approach 2:
The system performs preliminary analysis of captured images and provides immediate feedback on quality issues before final submission, allowing users to correct problems on the spot without requiring detailed pre-capture instructions.
3Measurement precision
If machine learning models process all video frames to ensure accurate document detection, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies machine learning models selectively to only those video frames that contain potential documents or show quality issues, rather than processing every frame, thereby reducing processing time while maintaining detection accuracy.
Solution Approach 2:
The video processing is segmented into multiple stages with different processing intensities: initial quick scanning of all frames, followed by detailed ML analysis only of frames containing documents or showing anomalies, efficiently balancing accuracy and speed.
4Reliability
If multiple images are captured and processed to generate a representative document image, then image quality and authenticity verification are improved, but processing complexity increases
Solution Approach 1:
The system merges multiple captured images into a single representative document image by aligning and combining corresponding regions, enhancing image quality and verification reliability while managing complexity through automated processing.
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.