OCR Image Segmentation for Automated Account Data Verification
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Solution Overview
Problem
Current OCR systems are inefficient and inaccurate in automatically processing and evaluating contextual and identification information from images, leading to increased processing time and costs, particularly in tasks like processing returned mail.
Innovation Solution
An OCR-based system utilizing enhanced image processing techniques, including image segmentation and machine learning, to optimize image quality, classify image types, and automatically execute actions associated with online accounts by linking extracted metadata to account information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR processing is used, then the system is simple to implement, but the processing accuracy and completeness are insufficient
Solution Approach 1:
The patent divides the image processing into distinct segments: image capture, image optimization, segmentation, OCR processing, and action execution. Each segment handles a specific task independently, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary image optimization and segmentation before OCR processing. By preparing the image in advance (correcting skew, adjusting contrast, segmenting regions), the subsequent OCR processing achieves higher accuracy without requiring a completely complex system.
2Productivity
If manual processing is used, then the system complexity is low, but the processing time and costs increase
Solution Approach 1:
The system automatically executes actions based on extracted information without requiring manual intervention. The automated workflow includes image capture, processing, information extraction, and direct execution of actions such as updating account information or triggering notifications, eliminating manual processing steps.
Solution Approach 2:
The patent replaces manual mechanical processing with automated computer-based processing. Manual evaluation of image information is substituted with automated OCR processing, machine learning classification, and programmatic action execution, significantly improving productivity.
3Measurement precision
If comprehensive image processing is applied, then the processing accuracy improves, but the processing time increases
Solution Approach 1:
The system applies different processing qualities to different regions of the image. Through segmentation, it identifies specific regions containing critical information (such as address fields or account numbers) and applies enhanced processing only to those regions rather than uniformly processing the entire image, reducing overall processing time while maintaining high accuracy for critical data.
Data Source
AI summary
Optical character recognition (OCR) based systems and methods for extracting and automatically evaluating contextual and identification information and associated metadata from an image utilizing enhanced image processing techniques and image segmentation. A unique, comprehensive integration with an account provider system and other third party systems may be utilized to automate the execution of an action associated with an online account. The system may evaluate text extracted from a captured image utilizing machine learning processing to classify an image type for the captured image, and select an optical character recognition model based on the classified image type. They system may compare a data value extracted from the recognized text for a particular data type with an associated online account data value for the particular data type to evaluate whether to automatically execute an action associated with the online account linked to the image based on the data value comparison.