Hand-Drawn Shape Recognition for Precise Template Conversion
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Solution Overview
Problem
Existing design applications struggle with imperfect geometric shapes drawn using imprecise drawing tools, leading to variations in line orientation, curvature, and angle deviations from intended geometric forms.
Innovation Solution
A computer-implemented method that receives user input to draw a shape, generates original drawing data, processes it into an input vector, classifies the shape using a machine learning model, and generates a new shape based on a recognized template shape.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users draw shapes manually using a draw tool, then users can freely create design elements, but the shapes will be imperfect with imprecise lines, curves, and angles
Solution Approach 1:
The patent introduces an intermediary processing system that acts as a mediator between the user's freehand drawing and the final template shape. The system processes the imperfect drawn shape through multiple stages: extracting geometric features, comparing against template shapes, and generating a corrected version. This intermediary process resolves the contradiction by preserving the ease of freehand drawing while eliminating precision errors through automated shape recognition and correction.
Solution Approach 2:
The patent replaces the mechanical precision requirement with an automated recognition system. Instead of requiring users to draw precisely (mechanical precision), the system uses image processing and pattern recognition algorithms to automatically identify the intended shape from imperfect input. This substitution of mechanical precision with computational recognition resolves the contradiction between ease of drawing and shape precision.
2Measurement precision
If the system processes original drawing data to recognize shapes, then shape recognition accuracy improves, but data processing requirements increase
Solution Approach 1:
The patent extracts only the essential geometric features from the complete drawing data, such as key points, line segments, angles, and curvature characteristics. By taking out only the necessary features rather than processing all raw data points, the system achieves accurate shape recognition while reducing data processing requirements. This extraction approach resolves the contradiction between recognition accuracy and data quantity.
Solution Approach 2:
The patent applies different processing quality levels to different parts of the drawing data. Critical features like intersection points and curvature extremes are processed with high precision, while less critical areas use simplified processing. This local quality approach maintains shape recognition accuracy for essential characteristics while reducing overall data processing requirements.
3Manufacturing precision
If users want to access template shapes, then shape precision improves, but the process becomes more complex requiring tool switching
Solution Approach 1:
The patent merges the draw tool and template shape selection into a single integrated workflow. Users draw shapes freely in the draw tool, and the system automatically recognizes and converts them to template shapes without requiring users to switch tools or manually search for templates. This merging of functions resolves the contradiction by maintaining template precision while eliminating the complexity of tool switching and manual template selection.
Solution Approach 2:
The system provides self-service by automatically recognizing the drawn shape and selecting the appropriate template shape without user intervention. The system serves itself by processing the drawing data, identifying the intended shape, and applying the correct template automatically. This self-service approach resolves the contradiction by maintaining precision while reducing process complexity through automation.
Data Source
AI summary
Described herein is a computer implemented method. The method includes receiving, via an input device, first user input drawing an input shape and generating, based on the first user input, original drawing data that includes an ordered set of points that define the input shape. The original drawing is processed to generate an input vector which also includes an ordered set of points. The input shape is then classified as a first template shape by processing the input vector using a machine learning model. A new shape is then generated based on the first template shape and the original drawing data.