Receipt Image Decoding Using Dictionary Matching and OCR
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Solution Overview
Problem
Current methods for collecting purchase data from consumers are manual, time-consuming, and resource-intensive, leading to reduced data capture and increased costs for marketing research entities.
Innovation Solution
The use of artificial intelligence techniques such as deep learning, computer vision, and natural language processing to automatically extract and decode purchase data from images of receipts, reducing the burden on panelists and improving data accuracy and throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual methods are used for collecting purchase data from consumers, then data can be collected with simple processes, but the time and resources required increase significantly
Solution Approach 1:
The patent replaces manual mechanical data entry processes with an automated image processing system using computer vision and optical character recognition (OCR) technologies. The system captures images of purchase documents, automatically extracts text and data, and processes it without human intervention, thereby eliminating the time-consuming manual data entry while maintaining operational simplicity for the user who only needs to take a photo.
Solution Approach 2:
The system enables self-service by allowing consumers to automatically submit purchase data through image capture. The automated extraction and processing mechanisms handle all data entry tasks without requiring consumer involvement in the actual data input process, making the system both simple to use and efficient in terms of time consumption.
2Productivity
If manual data entry is used by panelists, then resource consumption is high, but automation infrastructure is not required
Solution Approach 1:
The automated system serves multiple functions: capturing images of purchase documents, extracting text through OCR, structuring data, validating information, and submitting results. This multi-functional approach increases productivity significantly compared to manual entry, while the complexity is centralized in the processing system rather than requiring complex devices at each user endpoint—users only need a standard camera-capable device.
3Measurement precision
If automated image processing is used to extract purchase data, then data accuracy improves, but processing complexity increases
Solution Approach 1:
The system introduces an intermediary processing layer that bridges the simple image capture action and the final structured data output. This intermediary includes OCR engines, text extraction modules, and data validation components that handle the complexity of accurate data extraction, while users experience only the simplicity of taking a photo and receiving processed results.
4Reliability
If manual processing methods are used, then system complexity remains low, but error rates increase
Solution Approach 1:
The automated processing system incorporates feedback mechanisms including data validation rules, error detection algorithms, and quality checks that ensure high data accuracy. These feedback loops automatically identify and correct errors in extracted data, providing reliable results while the processing complexity is managed through systematic automated validation rather than manual verification.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed that decode purchase data using an image. An example apparatus includes a dictionary including associated product descriptions and barcodes, interface circuitry, and processing circuitry to execute machine readable instructions to obtain purchase details and barcodes corresponding to a receipt, the purchase details including receipt product descriptions, generate a search query that includes a first receipt product description of the receipt product descriptions, a list of barcodes corresponding to the barcodes, and a store identifier associated with the receipt, execute a search against the dictionary using the search query to identify a barcode from the list of barcodes that corresponds to the first receipt product description, and in response to identifying the barcode that corresponds to the first receipt product description, associating the barcode and the first receipt product description and adding the association to the dictionary.