Handwriting Stroke Segmentation for Vector Analysis
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Solution Overview
Problem
Current computerized writing evaluation methods on smart tablets lack the ability to perform vector or parameter analysis on handwriting, making it difficult to objectively assess the correctness and neatness of written fonts, as the information in strokes is not effectively parameterized or vectorized.
Innovation Solution
A computerized writing evaluation and training method that segments and divides written text into line segments or short arcs for vector or parameter analysis, allowing for the characterization of point coordinates and comparison with template text to determine correctness and neatness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If handwriting is displayed as point coordinates in two-dimensional space, then the handwriting can be captured and stored, but the vector or parameter analysis cannot be performed to evaluate correctness and neatness
Solution Approach 1:
The patent transforms handwriting stroke information from simple point coordinates into parameterized representations including line segments and short arcs. Each stroke is characterized by parameters such as start point, end point, control points, and geometric properties, enabling quantitative analysis of handwriting correctness and neatness while preserving essential stroke information.
Solution Approach 2:
The patent divides handwriting strokes into discrete segments (line segments and short arcs) that can be individually analyzed. By segmenting the continuous stroke data into manageable parameterized units, the system can perform precise vector analysis on each segment to evaluate handwriting quality without losing overall stroke information.
2Manufacturing precision
If handwriting strokes are represented as point coordinates, then the data can be stored, but it is impossible to judge correctness and neatness without vector or parameter analysis
Solution Approach 1:
The patent converts raw point coordinate data into parameterized stroke representations with defined geometric properties. This transformation enables systematic comparison between handwritten strokes and standard characters, providing an objective basis for evaluating correctness and neatness while maintaining manageable system complexity through standardized parameter structures.
Solution Approach 2:
The patent replaces subjective visual assessment with automated vector and parameter analysis. By substituting human judgment with computational analysis of stroke parameters, the system achieves objective and consistent evaluation of handwriting quality without requiring complex mechanical or optical scanning equipment.
3Adaptability or versatility
If smart tablet writing is used for font learning, then digital writing practice can be achieved, but the writing way is not natural and font size cannot be resized automatically
Solution Approach 1:
The patent implements dynamic font size adjustment that automatically adapts to the writer's handwriting scale. The system detects the actual size of handwritten characters and automatically resizes the display and evaluation parameters to match, providing a natural writing experience that adapts to the user's preferences while maintaining digital evaluation capabilities.
Data Source
AI summary
A computerized writing evaluation and training method is provided. First, a display interface unit displays a template text, and then, a writing input interface accepts a written text written by a writer, and afterwards, a processing unit segments the written text according to a stroke or a turning point and divides each stroke of the template text and the written text into a line segment or a short arc, and finally, the processing unit compares each stroke information of the template text and the written text and determines whether the written text is correctly written.