Neural Document Field Extraction for Flexible Image Data Capture
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Solution Overview
Problem
Existing image processing techniques for extracting information from captured images are inflexible and rely on rigid formats, leading to inefficiencies and the need for extensive human intervention.
Innovation Solution
A neural network system is employed to identify and extract information from captured images, utilizing modules such as facial recognition, character recognition, and field finding to determine and label document portions, allowing for flexible data extraction and reduced human involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional image processing techniques are used to extract information from captured images, then the extraction process can be performed, but the system requires extensive human intervention and has low efficiency
Solution Approach 1:
The system employs multiple neural networks that automatically perform different stages of data extraction without human intervention. The first neural network identifies document type and fields, the second extracts data from identified fields, and the third validates extracted data - all operations are self-executing, eliminating the need for manual processing while significantly improving productivity
Solution Approach 2:
The patent introduces neural networks as intermediary components between the captured image and the final extracted data. These neural networks act as automated mediators that bridge the gap between raw image data and structured information, replacing manual human intervention with intelligent automated processing systems
2Adaptability or versatility
If inflexible operations and rigid formats are used to extract information from captured images, then the extraction process can be standardized, but the system cannot adapt to different document types and formats
Solution Approach 1:
The system dynamically adapts its processing approach based on the document type identified by the first neural network. Instead of using a fixed rigid format, the system adjusts its extraction strategies, field identifiers, and validation rules according to the specific document type (e.g., invoice, receipt, bill of lading), thereby maintaining both adaptability across different formats and reliability through type-specific processing
Solution Approach 2:
The patent changes processing parameters based on document type classification. The neural networks adjust extraction parameters, field mappings, and validation criteria dynamically according to the identified document category, allowing the system to maintain high accuracy across diverse document formats by optimizing parameters for each specific type
3Measurement precision
If manual review and processing are performed to ensure data extraction accuracy, then the accuracy can be maintained, but the processing time increases significantly
Solution Approach 1:
The system performs extraction, validation, and error correction in continuous automated stages without interruption or manual review. The neural networks operate sequentially and continuously through all processing stages, maintaining high accuracy through automated validation while eliminating the time loss associated with manual review cycles
Solution Approach 2:
The third neural network provides automated feedback validation of extracted data, checking for accuracy and consistency. This feedback mechanism operates automatically without human intervention, maintaining measurement precision through intelligent validation while preventing time loss by eliminating manual review requirements
Data Source
AI summary
Computer systems and methods are provided for extracting information from an image of a document. A computer system receives image data, the image data including an image of a document. The computer system determines a portion of the received image data that corresponds to a predefined document field. The computer system utilizes a neural network system to assign a label to the determined portion of the received image data. The computer system performs text recognition on the portion of the received image data and stores the recognized text in association with the assigned label.