AI-Guided Scanner Rescanning for Accurate Document Parameters
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Solution Overview
Problem
Existing scanners require user input to select the type of document image, which can be cumbersome and subject to user error, leading to inappropriate parameter selection.
Innovation Solution
A scanner that includes a memory to store recommended parameter groups and communicates with a generative AI server to determine the document type automatically, allowing it to adjust scanning parameters based on learned models without user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the user manually selects the type of document image, then the scanner can apply appropriate correction parameters, but the operation becomes troublesome and time-consuming for the user
Solution Approach 1:
The scanner automatically detects the document type and selects appropriate correction parameters without requiring user input. The system serves itself by using the learned model to identify document characteristics and autonomously configure scanning settings, eliminating the need for manual user selection while maintaining accurate scanning results
Solution Approach 2:
The manual selection process is replaced by an automated image recognition system using a learned model. Instead of relying on user judgment and manual parameter selection, the system uses AI-based document type detection to automatically determine the appropriate scanning parameters, substituting human operation with intelligent automation
2Productivity
If the user selects the type of image based on their senses, then the selection is made quickly, but the selection may be inappropriate due to subjective judgment
Solution Approach 1:
The system uses a learned model to analyze the scanned image and provide feedback about the actual document type. This feedback loop allows the system to automatically adjust and select appropriate correction parameters based on objective image analysis rather than subjective user judgment, ensuring accurate parameter selection while maintaining quick processing speeds
Solution Approach 2:
The subjective user judgment process is replaced by an objective AI-based document type detection system. The learned model analyzes image features and automatically determines the correct document type, eliminating the variability and potential errors of human sensory judgment while maintaining rapid processing
3Adaptability or versatility
If the scanner requires user input for document type selection, then the system can handle various document types, but the complexity of the operation increases
Solution Approach 1:
The scanner automatically adapts to different document types by using the learned model to detect and identify them. The system serves itself by autonomously selecting appropriate correction parameters for various document types without requiring user input, maintaining versatility while simplifying the operation to a single scan action
Solution Approach 2:
The learned model provides a universal solution that can handle multiple document types through a single automated detection mechanism. Instead of requiring users to manually select from different document type options, the system universally applies AI-based detection that automatically identifies and configures settings for any document type, reducing operational complexity while maintaining broad adaptability
Data Source
AI summary
In a case in which a predetermined scan instruction is received via a user interface, the scanner is configured to execute: a first scan process of generating first scan data indicating a first image of a document based on a result of a first scanning; and a transmission process of transmitting the first scan data to a server. In a case in which type information indicating a type of image output by a learned model based on the first scan data is received from the server via the communication interface, the scanner is configured to execute a second scan process of executing a second scanning to scan the document with a recommended parameter corresponding to the type of image based on the type information received from the server, and generating second scan data indicating a second image of the document based on a result of the second scanning.