Transaction Record Image Processing With OCR Data Augmentation
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Solution Overview
Problem
Existing systems struggle to accurately process and classify low-quality images of financial transaction records, such as receipts, business cards, and travel itineraries, due to variability in image capture conditions and limitations of optical character recognition (OCR) technology.
Innovation Solution
A system and method that utilizes a combination of optical character recognition (OCR), external data sources, and a human workforce to classify and augment information from images, including interfaces to services like Amazon Mechanical Turk, to enhance the accuracy and completeness of record processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If optical character recognition (OCR) is used to process images of financial transaction records, then processing speed is improved, but accuracy deteriorates due to low-quality images and variability in image capture conditions
Solution Approach 1:
The processing system is segmented into multiple functional components: image quality assessment module, OCR processing module, data augmentation module, and classification module. Each component handles specific aspects of the processing pipeline, allowing the system to maintain high processing speed while improving accuracy through specialized handling of low-quality images and variability in capture conditions
Solution Approach 2:
External data sources and human annotators serve as intermediaries to supplement and correct OCR outputs. The system uses external databases and manual verification to fill gaps in information extracted from low-quality images, thereby maintaining both speed and accuracy
2Measurement precision
If external data sources and human resources are utilized to augment information, then accuracy is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing images to assess quality and prepare data structures before human review or external data integration. This staging approach organizes the complexity into manageable phases, reducing the apparent system complexity while maintaining high accuracy through comprehensive data augmentation
Solution Approach 2:
The processing system is designed with multi-functionality to handle various record types (receipts, business cards, travel itineraries) and image qualities through a unified architecture. This universal design reduces complexity by avoiding separate specialized systems for different tasks while maintaining high accuracy across diverse scenarios
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
Enables efficient and accurate processing of low-quality images by leveraging programmatic and human resources to supplement OCR, ensuring reliable extraction and classification of record information for financial transactions and expense management.
Implementation Method 1
perform optical character recognition (OCR) on the image to determine a first set of information items about the record
Data Source
AI summary
A computer system can implement a network service by receiving, from a computing device of a user, image data comprising an image of a record. The computer system can then execute image processing logic to determine a set of information items from the image. The computer system may then execute augmentation logic to process the record by (i) accessing a transaction database to identify a plurality of transactions made by the user, (ii) identifying a matching transaction from the plurality of transactions that pertains to the record, and (iii) resolving the set of information items using the matching transaction.