Hand-Drawn Project Outline Recognition for Automatic Code Generation
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Solution Overview
Problem
Current methods for implementing computer projects from hand-drawn drafts lack the ability to handle a wide variety of project types efficiently, accurately, and resource-safely, while being user-friendly for users without advanced technical knowledge.
Innovation Solution
A method and system utilizing Artificial Intelligence and Machine Learning, specifically deep convolutional neural networks, to recognize and classify elements from hand-drawn drafts, generate code, and create complex technologies such as web applications, cloud architectures, and neural networks directly from paper designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If hand-drawn drafts are used as input for computer project implementation, then ease of operation and user-friendliness improve, but manufacturing precision and accuracy deteriorate
Solution Approach 1:
The patent introduces an intermediary processing system that includes image capture, preprocessing, OCR text recognition, and element classification modules. This intermediary system bridges the gap between hand-drawn drafts and computer project implementation, converting informal drawings into structured data that maintains both user-friendliness and implementation accuracy.
Solution Approach 2:
The patent replaces manual mechanical interpretation of drawings with automated optical and computational systems. Optical character recognition (OCR) technology substitutes manual text extraction, while machine learning-based element classification replaces manual interpretation, thereby maintaining precision while preserving ease of operation.
2Productivity
If automated code generation from hand-drawn drafts is implemented, then productivity increases, but device complexity and technical requirements worsen
Solution Approach 1:
The patent segments the complex automated generation process into distinct functional modules: image capture module, preprocessing module, OCR text recognition module, element classification module, and code generation module. This segmentation reduces device complexity by making each module independent and manageable while maintaining high productivity through their coordinated operation.
Solution Approach 2:
The patent creates a universal system that can handle multiple types of computer projects (software applications, websites, mobile apps, cloud architectures) through a single integrated platform. The system uses universal element classification categories and standardized code generation templates, reducing technical requirements for users while maintaining high productivity across diverse project types.
3Adaptability or versatility
If comprehensive element classification is applied to handle various project types, then adaptability and versatility improve, but device complexity worsens
Solution Approach 1:
The patent adds a classification dimension to the processing system, where elements are categorized into different types (UI elements, data elements, functional elements, structural elements) based on their roles in various project types. This dimensional classification enables the system to handle diverse projects adaptably while maintaining manageable complexity through structured categorization rather than requiring separate processing paths for each project type.
Data Source
AI summary
It is provided a method, device and system to facilitate the design or and architecture of a computer project and its subsequent automatic implementation, based on a hand-written/drawn draft (e.g. on paper). This draft directly becomes the tool that will be used, automatically, as the basis for the final computer project implementation.