Receipt Text Data Word Detection via Keyword Relationships
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies for POS terminals are unable to effectively detect valuable information from printed receipts, which can be useful for further processing or analysis.
Innovation Solution
A program that deconstructs text data from printed receipts into words and detects specific information using keyword and relationship information, enabling accurate extraction of desired data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text data from printed receipts is processed using conventional methods, then the processing is simple, but the detection precision of useful information is insufficient
Solution Approach 1:
The text data processing is segmented into multiple stages: first deconstructing text data into individual words, then detecting keywords from those words, and finally identifying target information based on relationships between keywords. This segmentation allows complex detection to be broken down into manageable steps, improving detection precision without overwhelming system complexity.
Solution Approach 2:
The system performs preliminary deconstruction of text data into words before keyword detection. This preliminary action prepares the data in advance, making the subsequent keyword detection and relationship-based information extraction more accurate and efficient.
2Measurement precision
If keyword-based detection is implemented without relationship analysis, then the processing speed is fast, but the accuracy of detecting target information is insufficient
Solution Approach 1:
The detection process is segmented into hierarchical levels: word-level deconstruction, keyword-level detection, and relationship-level analysis. This segmentation enables the system to process information at multiple granularities, balancing speed and accuracy by quickly identifying keywords and then applying relationship analysis only where needed.
Solution Approach 2:
Keywords serve as intermediaries between the raw text data and the final target information. The system first extracts keywords as intermediate results, then uses relationships between these keywords to identify target information. This intermediary approach reduces processing time compared to direct analysis while maintaining high accuracy.
3Productivity
If all text data is analyzed in detail, then the detection accuracy is high, but the processing efficiency is low
Solution Approach 1:
The system extracts only the essential components from text data - first extracting words from text, then extracting keywords from words, and finally extracting target information from keyword relationships. This extraction approach avoids analyzing every detail of the text data, thereby maintaining high detection accuracy while significantly improving processing efficiency.
Solution Approach 2:
Instead of performing complete detailed analysis on all text data, the system applies partial action by focusing analysis only on extracted keywords and their relationships. This partial analysis approach achieves sufficient detection accuracy for practical purposes while dramatically reducing processing time and resource consumption.
Data Source
AI summary
Information that is printed can be correctly analyzed. A program that can be read and executed by the controller of a computer has a function of acquiring and deconstructing information that is printed by a printer as text data written as text, generating words; and a function of detecting from the generated words, based on keyword information and relation information describing a relationship between the keyword information and the word to detect, a target word to detect.