Dynamic Writing Panel for Handwriting Input Error Reduction
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Solution Overview
Problem
Existing user interfaces for handwriting input on mobile devices often lead to inadvertent selection of function keys during input, reducing the effective area for writing and increasing errors.
Innovation Solution
A user interface design where the writing panel expands to cover function keys upon detection of handwriting input, providing a larger area for input and preventing accidental key selection, while also using a recognition engine to present prediction candidates based on user history and lexicon for improved text construction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of moving object
If the writing panel area is increased to provide more space for handwriting input, then the effective writing area is improved, but the function keys may become inaccessible or the interface layout becomes complex
Solution Approach 1:
The writing panel dynamically changes its area based on the input state. When handwriting input is detected, the writing panel expands to a second area that is larger than the first area, covering the function keys to prevent inadvertent selection. When handwriting input is not detected, the writing panel returns to its original first area, making function keys accessible again. This dynamic adjustment resolves the contradiction by providing both large writing area and function key accessibility at different times.
2Reliability
If the writing panel expands to cover function keys to prevent inadvertent selection, then input accuracy is improved, but the function keys become inaccessible during writing
Solution Approach 1:
The system dynamically adjusts the writing panel area based on whether handwriting input is detected. During handwriting input, the expanded writing panel covers function keys to prevent accidental selections, improving input reliability. When handwriting input is not detected, the writing panel contracts to its original size, restoring function key accessibility. This time-based dynamic adjustment ensures both reliability during writing and accessibility when needed.
3Productivity
If the user interface provides both recognition candidates and prediction candidates, then text construction efficiency is improved, but the interface complexity increases
Solution Approach 1:
The system merges recognition candidates and prediction candidates into a single unified display area. Both types of candidates are presented together in the user interface, allowing users to select from recognized handwriting or predicted text continuations without switching between separate interfaces. This integration improves text construction efficiency while managing interface complexity through unified presentation.
Solution Approach 2:
The system performs preliminary recognition of handwriting input and generates both recognition candidates and prediction candidates before user selection. The recognition engine processes the handwriting input and generates candidate text, while the prediction engine uses lexicon and usage history to predict likely continuations. This preliminary processing allows both types of candidates to be ready for simultaneous presentation, improving text construction efficiency.
Data Source
AI summary
A method, medium, device, and user interface are provided for receiving a handwriting input and for presenting prediction candidates based on the handwriting input. A pen down event is detected in a writing panel of a user interface. The writing panel is expanded to overlie function keys and a candidate window of the user interface such that those features are made inaccessible for inadvertent selections thereof. Upon detection of a pen up event the writing panel is decreased in size to again reveal the features. Recognition candidates are identified based on the handwriting input. Prediction candidates are also identified based on a highest ranking recognition candidate and are presented in combination with the highest ranking recognition candidate. Prediction candidates are thereby identifiable with the recognition candidate with which they are associated and selection of a prediction candidate and recognition candidate may be performed in single selection input.