Neural Network Document Extraction for Varied Image Formats
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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 excessive 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, thereby improving data extraction accuracy and reducing human involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional image processing techniques are used to extract information from captured images, then the extraction process can be performed, but the system requires excessive human intervention and cannot handle varied document formats effectively
Solution Approach 1:
The system performs self-service by automatically determining document type, extracting relevant fields, and storing data without human intervention. The machine learning model autonomously identifies and processes different document formats, eliminating the need for manual format specification and human review.
Solution Approach 2:
The system changes parameters dynamically by adjusting the document type classification and field extraction parameters based on the input image characteristics. The machine learning model adapts its processing parameters according to the detected document format, enabling versatile handling of varied document types with a single unified system.
2Productivity
If rigid format requirements are imposed on captured images, then information extraction can be simplified, but the system becomes inflexible and requires image recapturing
Solution Approach 1:
The system implements dynamics by making the document processing pipeline adaptive rather than static. The machine learning model dynamically adjusts its behavior based on the input image quality and document type, processing various formats without requiring the image capture process to be rigid or repetitive.
Solution Approach 2:
The system performs preliminary action by pre-processing the captured image through automatic document type determination and quality assessment before extraction. This preliminary analysis enables the system to handle diverse formats upfront, eliminating the need for subsequent recapturing or manual format correction.
3Measurement precision
If manual review and verification of extracted data are performed, then extraction accuracy can be improved, but processing time and resource utilization increase
Solution Approach 1:
The system implements feedback through its machine learning architecture, where the model continuously learns from processing results and adjusts its extraction accuracy. The feedback mechanism enables high precision extraction without manual review, as the system self-corrects and improves based on accumulated processing experience.
Solution Approach 2:
The system replaces the mechanical process of manual review with an automated machine learning-based verification system. The neural network model performs extraction and validation computationally, substituting human manual verification with an efficient automated process that maintains high accuracy without the time cost of human intervention.
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.