Check Image Brightness Correction for Remote Deposit
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Solution Overview
Problem
The existing methods for remote deposit of checks face challenges in efficiently and accurately extracting information from digital images, requiring time-consuming and labor-intensive processes for payees to deposit funds.
Innovation Solution
A system that allows users to capture digital images of checks using imaging devices, which are then processed by financial institutions using techniques such as deskewing, dewarping, and brightness correction, enabling the extraction of relevant information for depositing funds into accounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If remote deposit is implemented without brightness correction, then the deposit process becomes faster and more convenient, but the accuracy of information extraction from check images deteriorates
Solution Approach 1:
The system performs brightness correction as a preliminary step before information extraction. By pre-processing the check image to correct brightness variations, the system ensures that subsequent OCR and data extraction operations can proceed accurately without requiring manual intervention, thus maintaining both speed and accuracy
Solution Approach 2:
Brightness correction acts as an intermediary processing step between image capture and information extraction. This intermediate processing layer transforms the raw image into a standardized format with uniform brightness, enabling accurate information extraction while keeping the overall process automated and efficient
2Measurement precision
If manual check deposit is used, then information extraction accuracy is maintained through human review, but the time and effort required for deposit increases
Solution Approach 1:
The system enables self-service remote deposit by automatically performing brightness correction and information extraction without requiring human intervention. The automated brightness correction ensures accurate extraction of check details, allowing payees to complete deposits quickly and independently without visiting bank branches
Solution Approach 2:
The system replaces manual mechanical processes (physical check handling, human visual inspection, manual data entry) with automated digital image processing. Brightness correction algorithms automatically adjust image quality, and OCR technology automatically extracts information, eliminating the need for manual review while maintaining accuracy
3Device complexity
If basic image processing is applied without brightness correction, then processing complexity is reduced, but the reliability of check information detection deteriorates
Solution Approach 1:
The image processing system is segmented into distinct functional modules: brightness correction, deskewing, dewarping, and information extraction. Each module performs a specific function, with brightness correction as a dedicated preprocessing step that improves detection reliability without requiring complex integrated processing
Data Source
AI summary
An image of a negotiable instrument may be taken by an imaging device and provided from a user to a financial institution. The image may be processed using operations such as deskewing the image, dewarping the image, and detecting corners or edges of the check in the image. Brightness correction may then be performed on the image. The brightness correction may be performed on the image using a histogram of the image with an overlaid reference mark. The negotiable instrument may be deposited in a user's account using the brightness corrected image.