Context-Based Shape Extraction from Hand-Drawn Ink Input
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing touch screen devices struggle to consistently and accurately interpret hand-drawn inputs beyond text, limiting their functionality in interpreting shapes, drawings, and non-text expressions.
Innovation Solution
A computerized method that receives user input, extracts shapes, determines associated entities based on context elements, and renders annotations using a shape model and annotation engine, enhanced by machine learning for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If touch screen devices use traditional handwriting interpretation methods, then text input recognition is achieved, but interpretation of shapes, drawings, and non-text expressions is limited
Solution Approach 1:
The system extends handwriting interpretation beyond traditional text recognition to handle multiple input types including shapes, drawings, symbols, and non-text expressions. The annotation engine is designed to process diverse input formats and convert them into meaningful annotations, making the touch screen device universally capable of interpreting various user expressions.
Solution Approach 2:
The patent introduces an intermediary processing layer that bridges raw touch input and final interpretation. This layer includes shape extraction algorithms and context analysis components that transform various input types into a standardized intermediate representation, which is then processed by the annotation engine to generate appropriate annotations.
2Ease of operation
If touch screen devices accept diverse hand-drawn inputs, then user flexibility is increased, but consistent and accurate interpretation becomes challenging
Solution Approach 1:
The system dynamically adjusts interpretation parameters based on the detected input type and context. Different extraction algorithms and recognition thresholds are applied depending on whether the input is identified as text, shape, drawing, or symbol. This adaptive parameter adjustment maintains high accuracy across diverse input types while preserving user flexibility.
Solution Approach 2:
The interpretation process is divided into distinct segmentation stages: input acquisition, shape extraction, pattern recognition, and annotation generation. Each stage processes specific aspects of the input independently, allowing the system to handle diverse inputs systematically while maintaining accuracy through specialized processing at each stage.
3Productivity
If handwriting interpretation is enhanced to recognize shapes and drawings, then communication capability is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary shape extraction and classification before full interpretation. By pre-processing inputs to identify basic shape characteristics and categories, the system reduces the complexity of subsequent recognition stages. This preliminary action enables faster processing and simplifies the overall system architecture while maintaining enhanced communication capabilities.
Data Source
AI summary
The electronic devices described herein are configured to enhance user experience associated with drawing or otherwise inputting shape data into the electronic devices. Shape input data is identified and matched against known shape patterns and, when a match is found, an entity associated with the shape is determined. The entity is converted into an annotation for rendering and/or displaying to the user. The shape identification, entity determination, and annotation conversion may all be based on one or more context elements to increase the accuracy of the shape interpretation. In particular, elements of conversations held via the electronic devices may be used as context for the shape interpretation. Further, machine learning techniques may be applied based on a variety of feedback data to improve the accuracy, speed, and/or performance of the shape interpretation process.