Neuronal Visual-Linguistic Mechanism for Receipt Data Extraction
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Solution Overview
Problem
Existing systems face challenges in accurately extracting expense data from receipt images due to variations in fonts, layouts, and scanning environments, making it difficult to retrieve information efficiently from imaged documents.
Innovation Solution
A method utilizing an automatic invoice analyzer with a dedicated OCR engine and a neuronal visual-linguistic mechanism, combining geometric and linguistic features through deep network architectures for semantic analysis, to perform content analysis and automatic information retrieval from invoice images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR and layout analysis methods are used, then the system structure remains simple, but the extraction precision deteriorates due to high variance in fonts and layouts
Solution Approach 1:
The patent combines multiple analysis functions (OCR, layout analysis, semantic understanding) into a single integrated deep neural network model. The network jointly processes image inputs and performs character recognition, layout detection, and semantic interpretation in unified architecture, resolving the contradiction by merging previously separate processing stages into one cohesive system that achieves high precision without proportional increases in system complexity
Solution Approach 2:
The patent transforms the processing approach by changing from traditional rule-based parameters to learned neural network parameters. The system uses trainable weights and biases that adapt to various font styles, layouts, and document types, enabling high extraction precision across diverse inputs while maintaining manageable system complexity through parameterized learning rather than explicit rule encoding
2Productivity
If manual data entry is used, then extraction precision can be high, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The system performs automatic self-service by using the deep neural network to independently extract and interpret data from invoice images without human intervention. The network automatically detects layouts, recognizes text, understands semantics, and structures output data, achieving both high productivity through automation and maintained precision through intelligent processing
Solution Approach 2:
The patent replaces manual mechanical data entry operations with an automated electronic system. The deep neural network substitutes human cognitive processes (reading, understanding, transcribing) with computational operations, dramatically improving productivity while maintaining or exceeding the precision that manual entry could achieve
3Adaptability or versatility
If simple OCR is used, then the device complexity remains low, but the semantic understanding capability deteriorates
Solution Approach 1:
The patent segments the semantic understanding task into distinct functional components within the neural network architecture. The system divides processing into character recognition, word formation, phrase identification, and semantic interpretation stages, with each segment handled by specialized network layers. This segmentation enables sophisticated semantic analysis capability while managing architectural complexity through modular functional decomposition
Data Source
AI summary
Systems and methods for automatic information retrieval from imaged documents. Deep network architectures retrieve information from imaged documents using a neuronal visual-linguistic mechanism including a geometrically trained neuronal network. An expense management platform uses the neuronal visual-linguistic mechanism to determine geometric-semantic information of the imaged document.