Editable Image Creation from Static Diagrams
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Solution Overview
Problem
Current digital conversion of static images, such as hand-drawn diagrams, results in resolution-dependent formats that cannot be easily scaled or manipulated, leading to scaling issues, limited editing capabilities, and compatibility problems between drawing programs, making it difficult to correct or alter images without rewriting them completely.
Innovation Solution
A method and system that processes raw static images using image acquisition devices, noise reduction, line recognition, object identification, and shape classification to create editable images, allowing users to correct and manipulate the content by superimposing digitally recreated objects over the original image, enabling clean breaks, smooth edges, and consistent font and color matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static images are converted to digital formats (bitmap, JPEG, TIFF, PNG), then the images can be stored and displayed, but they become resolution-dependent and cannot be scaled without loss of quality
Solution Approach 1:
The patent segments the image into distinct geometric elements (lines, curves, shapes, text) and represents each element with its own mathematical parameters. This segmentation allows independent manipulation and scaling of each element without affecting others, resolving the contradiction between digital storage and quality preservation.
Solution Approach 2:
The patent transforms the image from a fixed-resolution bitmap representation to a parameter-based vector representation where geometric properties (coordinates, dimensions, angles) are stored as editable parameters. This parameter change enables infinite scaling and editing while maintaining quality, as the image is regenerated from parameters rather than stretched pixels.
2Ease of operation
If layers are applied over the digitized image to enable editing, then some manipulation capability is added, but the editing remains limited and cannot achieve clean breaks, smooth edges, or consistent formatting
Solution Approach 1:
The patent transforms the static digitized image into a dynamic editable structure where geometric elements can be freely modified, moved, and reconfigured. Each element maintains its mathematical definition, allowing dynamic adjustment of positions, sizes, and shapes while automatically preserving smooth edges and clean breaks through regenerative rendering from parameter definitions.
3Ease of manufacture
If the image is scanned or captured at a slight angle, then the image can be digitized, but it becomes difficult or impossible to right the image until it is square on the screen
Solution Approach 1:
The patent performs preliminary geometric analysis and transformation during the digitization process, automatically detecting and correcting angular deviations. By establishing the geometric relationships and parameters of the image elements during initial capture, the system prepares the data in a corrected orientation, eliminating the need for subsequent manual alignment operations.
4Adaptability or versatility
If material is added to the image or parts are rearranged, then the content can be modified, but it is difficult to achieve clean breaks, smooth edges, or match font sizes and colors
Solution Approach 1:
The patent enables content manipulation through parameter modification of geometric elements. When material is added or rearranged, the system uses parameter-based definitions to automatically maintain consistent formatting, smooth edges, and uniform styling across all elements. The mathematical parameter representation ensures that all modifications adhere to the same quality standards as the original elements.
Data Source
AI summary
A method of creating an editable image and editable text from a hand-drawn or other static two-dimensional diagram may include receiving a raw image from an image acquisition device; modifying the raw image to a modified image to reduce noise, normalize raw image data, and reduce pixels; recognizing horizontal, vertical, and diagonal lines in the modified image using a line recognizer; connecting the lines in the modified image to form connected lines using a connector that detects and joins proximally positioned terminal ends of the lines in the modified image; recognizing areas bounded by the connected lines as bounded objects using a bounded object recognizer; and/or identifying and classifying the bounded objects using an object identifier.