Handwritten Diagram Recognition via Deep Learning
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Solution Overview
Problem
Current methods for converting handwritten diagrams into digital models are tedious and require significant manual effort, as they involve either archiving photos or recreating sketches using diagram modeling software, lacking efficient automation for recognizing symbols and connections in graphical modeling languages.
Innovation Solution
A deep learning system that includes a machine learning component for object localization and classification, a structure recognition component for identifying connections, and a handwriting recognition component to generate a digital structured model from handwritten diagrams, using convolutional neural networks and region-based CNN architectures to detect symbols, text, and arrow keypoints, thereby automating the conversion process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If handwritten diagrams are converted using manual methods (photo archiving or recreation in software), then the process is simple to implement, but it requires substantial time and manual effort
Solution Approach 1:
The patent replaces manual mechanical processes (hand-drawing, photo archiving) with an automated computer vision system that uses deep learning models to detect, recognize, and convert handwritten diagrams into digital formats automatically, eliminating the need for manual intervention
Solution Approach 2:
The system enables the handwritten diagram itself to be the source of its own digital conversion by automatically detecting symbols, text, and connections without requiring external manual input, making the conversion process self-service rather than dependent on human operators
2Extent of automation
If deep learning models are used for automatic recognition, then conversion automation is achieved, but system complexity increases
Solution Approach 1:
The patent segments the complex recognition task into distinct functional modules: a machine learning component for object localization and classification, a structure recognition component for identifying connections, and a handwriting recognition component for text interpretation, allowing each module to specialize in specific functions
Solution Approach 2:
The patent introduces intermediate processing stages including object detection outputs that feed into structure recognition, and separate handwriting recognition that maps text to detected objects, creating a structured pipeline that manages complexity through intermediate representation layers
3Measurement precision
If comprehensive object detection and classification is performed, then recognition accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary object detection and classification to identify all diagram elements (symbols, text blocks, arrows) before proceeding to structure recognition and connection analysis, allowing subsequent processing to focus on relationships rather than basic identification
Solution Approach 2:
The patent applies different levels of processing intensity to different parts of the diagram, using comprehensive detection for all objects but focusing detailed analysis only where needed for connection recognition and text-to-symbol mapping
Data Source
AI summary
Systems, methods, and devices are described herein for handwritten diagram recognition using machine learning. A machine learning component receives a digitally encoded image having a handwritten diagram. The machine learning component localizes and classifies a plurality of objects within the handwritten diagram. A structure recognition component identifies connections between each symbol of the plurality of objects based on content of the respective object. A handwriting recognition component interprets one or more alphanumeric text strings within a portion of the plurality of objects. A digital structured model of the digitally encoded image is automatically generated, without human intervention. The digital structured model has the identified connections among the plurality of objects and is in a computer-readable editable format.