Handwriting Correction Electronic Device Nonlinear Distortion
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Solution Overview
Problem
Existing electronic devices are limited in correcting handwriting data distortion, as they primarily focus on linear corrections and are designed for specific languages, failing to adequately consider the diversity of handwriting characteristics such as size, height, and connection structures across various languages.
Innovation Solution
An electronic device that learns to correct handwriting data distortion through a data-driven approach, using a processor to align letters with a reference line, change positions or angles, and store correction information for nonlinear distortions, enabling language-independent correction of handwriting inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing linear correction methods are used, then correction simplicity is maintained, but correction accuracy for diverse handwriting characteristics deteriorates
Solution Approach 1:
The patent transforms the correction approach by changing from fixed linear parameters to dynamic nonlinear parameters. The system learns optimal correction parameters through training with distorted handwriting data, allowing parameters to adapt to different handwriting styles, sizes, and languages, thereby improving accuracy while managing complexity through parameter optimization
Solution Approach 2:
The system performs preliminary training offline to learn correction patterns from various handwriting distortions. By pre-learning the nonlinear relationships between distorted and correct handwriting characteristics, the system prepares correction models in advance, enabling accurate real-time correction without complex online computation
2Adaptability or versatility
If language-specific correction rules are applied, then correction effectiveness for specific languages is improved, but adaptability to various languages deteriorates
Solution Approach 1:
The patent creates a universal correction system that can handle multiple languages and handwriting styles through a single trained model. By training on diverse handwriting data from various languages and scripts, the system learns language-agnostic nonlinear correction patterns, enabling it to adapt to new languages without requiring language-specific rule sets while maintaining high correction effectiveness
Solution Approach 2:
The system learns to copy the essential structural characteristics of correct handwriting from training data. By capturing the underlying patterns of proper letter formation, spacing, and alignment across different languages, the system can replicate these patterns in corrected output, achieving both universality and precision through pattern copying rather than rule-based transformation
3Adaptability or versatility
If only baseline histogram methods are used, then processing simplicity is maintained, but consideration of handwriting diversity such as size, height, and connection structure deteriorates
Solution Approach 1:
The patent extends the correction approach from one-dimensional baseline alignment to multi-dimensional handwriting characteristics. The system simultaneously corrects letter size, height, position, connection structures, and angular orientations by learning nonlinear transformations across multiple dimensions, thereby comprehensively addressing handwriting diversity while managing processing complexity through integrated dimensional correction
Data Source
AI summary
Disclosed is an electronic device including: a memory, and a processor operatively connected to the memory. The memory stores instructions which, when executed, cause the processor to: obtain handwriting data including at least one letter; align the at least one letter with a reference line to generate target handwriting data; change at least one of a position or an angle of the at least one letter to generate distorted handwriting data; obtain correction information for correcting the distorted handwriting data to correspond to the target handwriting data; and store the correction information in the memory.