Scanner Parameter Recommendation Using ML for Document-Specific Scans
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Solution Overview
Problem
Existing scanners adjust show-through correction based on user-selected image type, limiting the optimization of scanning results for the original document.
Innovation Solution
A scanner that prompts users with questions through a user interface, transmits question-and-answer information to a server, and uses a trained machine learning model to generate recommended parameter sets for improved scanning results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the scanner adjusts show-through correction based on user-selected image type, then the operation is simple and quick, but the scanning result optimization is limited and may not be suitable for the original document
Solution Approach 1:
The scanner automatically determines image type and generates optimal scan parameters by itself, without requiring user selection. The system uses an AI model to analyze the original document and autonomously set correction amounts and other parameters, making the scanner self-sufficient while improving scanning quality.
Solution Approach 2:
The system dynamically changes multiple scan parameters (not just show-through correction) based on AI analysis of the original document. The AI model determines optimal values for resolution, correction amounts, and other parameters, allowing flexible adaptation to different document types and conditions.
2Manufacturing precision
If the scanner uses a trained machine learning model to generate recommended parameter sets, then the scanning result quality is improved, but the device complexity increases
Solution Approach 1:
The AI model acts as an intermediary between the scanner hardware and the user. It processes the original document information and translates it into optimal scan parameters, serving as a bridge that simplifies the overall system architecture while improving quality.
Solution Approach 2:
The AI model is pre-trained and stored in the scanner before use. When scanning, the model is already ready to analyze the original document and generate parameters immediately, eliminating the need for complex real-time processing or external connections during the actual scanning operation.
3Manufacturing precision
If the scanner prompts users with questions and transmits data to a server, then the parameter optimization is enhanced, but the scanning time and processing steps increase
Solution Approach 1:
The AI model is pre-loaded in the scanner and performs analysis locally without requiring server connections. The model is ready to process the original document and generate parameters immediately, eliminating network transmission time and server processing delays.
Solution Approach 2:
The scanner performs all necessary analysis and parameter generation autonomously using its built-in AI model. It doesn't need to prompt users with questions or transmit data externally, completing the optimization process internally and rapidly.
Data Source
AI summary
A scanner includes a scanning engine. The scanner outputs one or more questions related to generation of scan data through a user interface, and receives one or more answers through the user interface when a scan instruction is received. The scanner performs transmits question-and-answer information to a server through a communication interface. The question-and-answer information includes the questions and answers, and associates each question with a corresponding answer. The scanner performs a scan process when a recommended parameter set is received from the server through the communication interface. The recommended parameter set is outputted by a trained machine learning model based on the question-and-answer information. The scan process is based on the recommended parameter set. The scan process includes reading an original using the scanning engine to generate scan data. The scanner outputs the scan data or an object based on the scan data.