Remote Check Deposit OCR Sequencing for Low-Bandwidth Validation
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Solution Overview
Problem
Conventional remote check deposit systems face challenges in capturing clear and precise images of checks, particularly on resource-constrained devices, leading to user frustration and inefficiencies in optical character recognition (OCR) processes.
Innovation Solution
A method and system for remote check deposit using a mobile device that performs optical character recognition (OCR) locally, verifies image quality, and provides user-controlled image capture, followed by server validation, ensuring privacy and efficiency by minimizing network data transmission and offloading processing to the mobile device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If remote check deposit systems use traditional OCR processes on resource-constrained mobile devices, then check data extraction can be performed, but processing speed and accuracy deteriorate due to device limitations
Solution Approach 1:
The patent segments the OCR processing function into two parts: a lightweight preliminary OCR engine runs on the mobile device to extract and validate check data, while a more comprehensive OCR engine runs on the financial institution server for final validation. This segmentation allows the mobile device to perform basic extraction without being burdened by complex processing, thereby maintaining both acceptable accuracy and processing speed on resource-constrained devices.
2Measurement precision
If users manually review and recapture check images to ensure quality, then image quality improves, but user time and operational complexity increase
Solution Approach 1:
The system implements automated image quality assessment that performs self-service validation of captured check images. The mobile device automatically evaluates image quality metrics and determines whether the captured image meets deposit requirements, eliminating the need for users to manually review and recapture images. This self-service approach maintains high image quality standards while significantly reducing the time and effort required from users.
Solution Approach 2:
The system provides immediate feedback to users about image quality after capture. The automated assessment delivers clear guidance on whether the image is acceptable or needs improvement, allowing users to make quick adjustments if necessary. This feedback mechanism reduces unnecessary manual review and recapture cycles by providing objective quality measurements.
3Reliability
If check images are transmitted to financial institution servers for validation, then security and validation improve, but network bandwidth consumption increases
Solution Approach 1:
The patent extracts and validates essential check data locally on the mobile device using a lightweight OCR engine before transmitting anything to the server. Only the extracted data and minimal validation results are sent to the financial institution server, rather than transmitting the full high-resolution check image. This extraction approach maintains security and validation reliability while dramatically reducing network bandwidth consumption.
4Productivity
If mobile devices perform comprehensive OCR processing locally, then processing speed improves, but device energy consumption and complexity increase
Solution Approach 1:
The mobile device performs partial OCR processing - enough to extract and validate essential check data fields and determine image quality adequacy - but not comprehensive processing of the entire image. This partial action approach provides sufficient processing speed improvement for the user experience while limiting energy consumption to acceptable levels for mobile devices.
Data Source
AI summary
Methods and systems for remote check deposit are disclosed. A check for deposit is processed without the need for a server to receive any image of the check initially. Instead, optical character recognition (OCR) data is received at the server from a mobile device. Verification processing for the check is then performed using the OCR data. If the verification process is successful, a confirmation notification is sent to the mobile device. Subsequently, after sending the confirmation notification, a check image is received, from which the OCR data was determined. The check is, in turn, processed for deposit using the received check image.