Receipt Data Interpretation via Tokenized OCR and Template Matching
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Solution Overview
Problem
Receipts and invoices are often cryptic and difficult to interpret due to limited space and varying descriptive languages, making it challenging for third parties to understand the items purchased or services rendered.
Innovation Solution
An automated system processes receipt images using optical character recognition (OCR) to generate machine-encoded text, identify tokens, and construct data groups, which are then evaluated to accurately interpret the content, with potential human validation to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If receipts use limited space and vendor-specific descriptive language, then the receipt can be printed on narrow slips with multiple items, but the content becomes cryptic and difficult for third parties to interpret
Solution Approach 1:
The patent introduces an intermediary processing system that translates cryptic vendor-specific receipt language into understandable item descriptions. The system uses token generation, data group construction, and template matching to bridge the gap between compact vendor formatting and human-readable interpretations, allowing third parties to understand receipt contents without requiring vendor-specific knowledge
Solution Approach 2:
The patent adds a new dimension of interpretation by creating multiple layers of data representation: the original compact receipt data, tokenized representations, constructed data groups, and final interpreted item descriptions. This multi-dimensional approach allows the same information to serve both space-efficient storage and comprehensive interpretation needs
2Measurement precision
If an automated system processes receipts through multiple stages including OCR, token generation, data group construction, and template matching, then interpretation accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the receipt processing task into distinct modular stages: OCR for text extraction, token generation for data normalization, data group construction for structural organization, and template matching for interpretation. Each stage handles a specific aspect of processing, improving overall accuracy while allowing independent optimization and maintenance of each component
Solution Approach 2:
The system incorporates feedback mechanisms where processing results from each stage inform subsequent stages. Confidence scores from template matching feed back into the system to identify cases requiring additional processing or human review, creating a self-correcting workflow that improves accuracy without requiring all components to operate at maximum complexity simultaneously
Data Source
AI summary
Systems and methods for automatic processing of receipts to capture data from the receipts are presented. Upon receiving an image of a receipt, an optical character recognition (OCR) of the receipt content embodied in the image is executed. The OCR of the receipt content results in machine-encoded text content of the receipt content embodied in the image. Tokens are generated from the machine-encoded text content and data groups are constructed according to horizontal lines of generated tokens. Potential product items are identified for at least some of the constructed data groups and the potential product items are evaluated for the at least some constructed data groups. The evaluation of the potential product items for the at least some constructed data groups identifies receipt data, such as product items and vendor information, associated with the least some constructed data groups. The identified receipt data is captured and stored with the image of the receipt in a data store.