Receipt OCR Processing with Logo-Guided Image Cleanup
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Solution Overview
Problem
Conventional optical character recognition (OCR) techniques fail to reliably extract data from receipts due to varying fonts and formats used by different merchants, unlike the standardized formats of checks.
Innovation Solution
A system and method for processing receipts using image cleanup, logo detection, and text extraction, involving iterative algorithms to optimize image parameters for logo and text recognition, and associating extracted text with predefined receipt structures to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional OCR techniques are used to extract data from receipts, then the processing method is simple, but the reliability and accuracy of data extraction fail due to varying fonts and formats
Solution Approach 1:
The patent applies parameter changes by iteratively adjusting image processing parameters (such as contrast, brightness, and other visual characteristics) to optimize the appearance of receipt text and logos. This transformation of image parameters enables conventional OCR to reliably extract data from receipts with varying fonts and formats, resolving the contradiction between simple processing and reliable extraction.
2Measurement precision
If iterative algorithms are used to optimize image parameters for logo and text recognition, then the accuracy of text extraction is improved, but the processing time and computational load increase
Solution Approach 1:
The patent applies preliminary action by performing image parameter optimization and logo detection before the actual text extraction process. The iterative algorithms preprocess the receipt image to enhance text visibility and identify logos, which then guides the subsequent OCR process. This staged approach improves extraction accuracy while managing processing time by performing computations in a structured sequence rather than all at once.
3Manufacturing precision
If predefined receipt structures are used to associate extracted text, then the organization and accuracy of extracted data is improved, but the complexity of data processing and matching increases
Solution Approach 1:
The patent applies segmentation by dividing the receipt structure into predefined fields and regions (such as header, item list, total, etc.). The extracted text is then systematically matched to these predefined segments, which simplifies the organization process. This segmentation approach improves the precision of structured data extraction while reducing processing complexity by providing a clear template for data mapping rather than requiring complex analysis of the entire receipt structure.
Data Source
AI summary
Techniques for providing improved optical character recognition (OCR) for receipts are discussed herein. Some embodiments may provide for a system including one or more servers configured to perform receipt image cleanup, logo identification, and text extraction. The image cleanup may include transforming image data of the receipt by using image parameters values that optimize the logo identification, and performing logo identification using a comparison of the image data with training logos associated with merchants. When a merchant is identified, a second image clean up may be performed by using image parameter values optimized for text extraction. A receipt structure may be used to categorize the extracted text. Improved OCR accuracy is also achieved by applying on format rules of the receipt structure to the extracted text.