Logogram Candidate Generation via Stroke Recognition
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Solution Overview
Problem
Users face difficulties in accurately entering logograms on electronic devices due to uncertainty in rendering desired characters, as existing systems fail to effectively recognize and generate candidate logograms from stroke inputs.
Innovation Solution
An apparatus and method that utilize a graphical input, display, and processor to recognize one or more strokes of a logogram input, generating candidate logograms with matching strokes and topologies, simplifying the entry process by providing a list of potential logograms for selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually enter logograms character by character, then input accuracy can be achieved, but input time and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary stroke recognition and generates candidate logograms before the user completes the full character input. By analyzing strokes in real-time and presenting candidates during the input process, the system reduces the time required while maintaining accuracy through user confirmation of pre-generated candidates.
Solution Approach 2:
The system provides continuous feedback by displaying candidate logograms based on recognized strokes. This feedback loop allows users to confirm or correct the recognized character, ensuring input accuracy while significantly reducing the time required compared to manual character entry.
2Productivity
If the system generates candidate logograms from stroke inputs, then input speed increases, but recognition accuracy may deteriorate due to incomplete or ambiguous stroke data
Solution Approach 1:
The system generates candidate logograms based on partial stroke inputs rather than requiring complete characters. By allowing candidate generation from incomplete stroke data and presenting multiple possibilities, the system maintains high input speed while managing recognition accuracy through user selection from candidates.
Solution Approach 2:
The system changes the recognition parameter from requiring complete character strokes to accepting partial stroke sequences. This parameter change enables faster input by generating candidates from incomplete data, while accuracy is maintained through the candidate presentation and user confirmation mechanism.
3Measurement precision
If the system requires users to know the complete character set, then input precision improves, but ease of operation deteriorates for unfamiliar characters
Solution Approach 1:
The system provides self-service by automatically generating candidate logograms from user-provided strokes without requiring users to know the complete character set. Users simply input strokes and select from generated candidates, making the system accessible to users with limited knowledge of logogram characters while maintaining input precision through candidate confirmation.
4Ease of operation
If the system recognizes strokes and generates candidates in real-time, then user experience improves, but computational complexity and processing time increase
Solution Approach 1:
The system segments the logogram recognition process into discrete stroke recognition steps rather than analyzing complete characters at once. This segmentation allows for simpler, faster processing of individual strokes while maintaining good user experience through real-time candidate generation and presentation.
Data Source
AI summary
For generating candidate logograms, code recognizes one or more strokes of the logogram input. The code further generates one or more candidate logograms with strokes matching the logogram input strokes.