Intelligent Receipt Scanning System for Product Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Receipt analysis is challenging due to limited and unclear information, making it difficult to comprehend and accurately extract product details from consumer receipts, such as product codes and labels.
Innovation Solution
An intelligent receipt scanning and analysis system utilizing a processor-based system with a receipt scanner, scan transformer, information extractor, product matcher, and report generator, which employs machine learning to transform receipt information into text, extract meaningful details, compare them to known products, and generate reports on product identification, pricing, and market analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional receipt scanning methods are used, then the process is simple, but the accuracy of information extraction is low
Solution Approach 1:
The system segments the receipt processing task into distinct functional modules: receipt scanner for image capture, scan transformer for image-to-text conversion, information extractor for data extraction, product matcher for product identification, and report generator for output. Each module handles a specific aspect of the processing pipeline, improving overall extraction accuracy while organizing system complexity into manageable components.
Solution Approach 2:
The patent introduces intermediate processing layers between simple scanning and final analysis. The scan transformer acts as an intermediary converting images to text, while the information extractor serves as another intermediary that processes the text to extract structured data. These intermediary components bridge the gap between raw receipt data and actionable insights, significantly improving information extraction accuracy.
2Measurement precision
If detailed product information is extracted from receipts, then product identification accuracy improves, but resource utilization increases
Solution Approach 1:
The system extracts only the necessary product information fields required for identification purposes rather than processing every detail on the receipt. The product matcher compares extracted information against known product databases with targeted queries, performing partial action on the data set to achieve sufficient identification accuracy without exhaustive resource consumption.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the specific receipt and product type. The information extractor focuses on extracting only relevant parameters (product name, price, quantity, etc.) rather than all possible data points. This selective parameter extraction maintains high identification accuracy while reducing computational resources required for processing.
3Measurement precision
If machine learning processes are used to extract information, then extraction accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary processing of receipt images through the scan transformer to convert images to text before the information extractor applies machine learning algorithms. This preliminary action prepares the data in a more processable format, allowing the ML models to work more efficiently and reduce overall processing time while maintaining high extraction accuracy.
Solution Approach 2:
The patent replaces manual information extraction methods with automated machine learning-based extraction. The information extractor uses trained ML models to automatically identify and extract product information from receipt text, substituting the mechanical/manual process with an intelligent automated system that achieves high accuracy without proportional increases in processing time.
Data Source
AI summary
Intelligent receipt scanning and analysis may include scanning a receipt that includes information related to a product. The information related to the product may be transformed to text, and extracted from the text by utilizing a machine learning process. The extracted information may be compared to known information for a plurality of known products to identify the product as a known product of the plurality of known products or an unknown product. The extracted information related to the product and known product information, from the known information, for the known product of the plurality of known products may be analyzed in response to a determination that the extracted information is similar to the known product information for the known product of the plurality of known products.