OCR Transaction Data Verification and Error Correction
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Solution Overview
Problem
Manual input of financial transaction data from paper or electronic statements is prone to errors and time-consuming, especially when transactions span across multiple statements from different sources, making accurate reconciliation and organization difficult.
Innovation Solution
A system and method that uses Optical Character Recognition (OCR) to preprocess and convert statement images into text, verify data accuracy through error correction procedures, and organize transactions into a database for efficient sorting, filtering, and searching, utilizing multiple core processors for quick processing and high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual input of financial transaction data from paper or electronic statements is used, then data can be entered into a tabulated format, but errors are easily introduced and processing is time-consuming
Solution Approach 1:
The patent replaces the mechanical manual data entry process with an automated Optical Character Recognition (OCR) system that converts images of financial statements into structured text data. This substitution eliminates human error in data transcription and significantly reduces processing time while maintaining high accuracy through automated verification procedures.
Solution Approach 2:
The system performs self-verification of extracted data by automatically checking for errors and inconsistencies in the converted transaction data. The automated error correction procedures allow the system to self-correct mistakes without requiring manual review of each transaction, thereby maintaining high accuracy while minimizing processing time.
2Loss of information
If transactions from multiple statements are reconciled and organized manually, then comprehensive transaction history can be compiled, but the process becomes difficult and impossible to efficiently search and regroup
Solution Approach 1:
The patent creates a universal digital database structure that can accommodate transactions from multiple different statement formats and sources. The standardized tabulated format allows for efficient searching, filtering, and regrouping of transactions across different accounts and time periods, eliminating the limitations of physical document organization.
Solution Approach 2:
The system creates digital copies of transaction data from various statement formats and consolidates them into a unified database. This digital replication allows for easy manipulation, sorting, and searching of transaction histories without the physical constraints of handling multiple paper or PDF documents.
3Productivity
If OCR is used to convert statement images to text quickly, then processing time is reduced, but errors in conversion may occur
Solution Approach 1:
The patent implements automated feedback mechanisms where the OCR conversion results are automatically verified against expected data patterns and formats. The system checks for errors in the converted transaction data and triggers correction procedures when inconsistencies are detected, thereby maintaining high reliability while preserving fast processing speeds.
Solution Approach 2:
The system performs preliminary error checking and verification immediately after OCR conversion but before final data processing. By anticipating potential conversion errors and addressing them proactively through automated verification procedures, the system maintains both high speed and high accuracy in transaction data processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Ensures near 100% accuracy in data input, reduces processing time, and allows for efficient manipulation, filtering, and searching of financial transactions, with the ability to identify and correct errors automatically.
Implementation Method 1
The uploaded statement images are preprocessed and converted to text using Optical Character Recognition (OCR) to identify transactions and transaction types.
Data Source
AI summary
In a system and method for auditing transactions where a set of image based transactions are received over a communications network, and stored in a central data store, the set of image based transactions are associated with a unique identifier associated with a user. A transaction format is identified from the set of image based transactions, utilizing a processor to apply a preprocessing to the set of image based transaction based on the identifying. The preprocessed image based transactions are processed into a series of text based transactions, wherein each image based transaction has a related text based transactions and each text based transaction has a plurality of data representing the transaction. The plurality of data for each text based transaction is stored and a quality identifier is associated with each text based transaction. An identifier is applied to a text based transaction based on the quality identifier.