Editable Document Conversion Using Layout, Font, and Color Detection
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Solution Overview
Problem
Existing systems lack mechanisms for efficiently and accurately converting non-editable documents to editable documents, particularly in terms of replicating fonts, color schemes, and layouts, due to the complexity of automatically detecting and incorporating these elements.
Innovation Solution
A pipeline utilizing modules for font recommendation, media and color palette recommendation, and layout prediction, combined with optical character recognition, to identify and replicate the features of a non-editable document in an editable format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a user manually replicates a design from a sample document, then the resulting document can be edited and customized, but the process is time-consuming and requires speculation on colors, text types, and item dimensions
Solution Approach 1:
The system automatically copies the visual appearance and structural layout from a sample document to create an editable document. Machine learning models analyze the sample document to identify fonts, colors, shapes, and their arrangements, then replicate these elements in the generated editable document, eliminating manual replication efforts
Solution Approach 2:
The system performs self-service by automatically analyzing the sample document and generating the editable document without requiring user intervention for font selection, color matching, or layout recreation. The machine learning models autonomously complete tasks that would otherwise require extensive manual effort and speculation
2Adaptability or versatility
If a user searches through numerous publicly available designs to find a suitable sample, then a diverse range of design options can be explored, but the process becomes time-consuming and inconvenient
Solution Approach 1:
The system enables direct copying from any visual sample document regardless of its source or format. Users can upload any image or document as a sample, and the system will analyze and replicate its design elements, providing versatility in design sources without requiring time-consuming searches through multiple platforms
3Productivity
If machine learning models are used to automatically identify fonts, colors, and layouts from a non-editable document, then the conversion to editable document is quick and accurate, but the system complexity increases
Solution Approach 1:
The system segments the complex task of document conversion into distinct machine learning models for different functions: one model identifies fonts, another identifies colors, and another identifies layout elements. This segmentation allows each model to specialize in a specific aspect, improving accuracy while making the overall system more manageable and modular
Solution Approach 2:
The system uses machine learning models as intermediary components between the non-editable document and the editable output. These models act as mediators that analyze the visual elements and translate them into editable format specifications, bridging the gap between static images and editable documents without requiring direct manual intervention
Data Source
AI summary
A system and method and for generating an editable document from a non-editable document includes automatically identifying a layout for a plurality of objects in the non-editable document, where the layout includes bounding boxes of one or more of the objects, and automatically identifying a font for a text object of the plurality of objects. The method and system also includes selecting a color scheme for one or more of the plurality of objects, the color scheme corresponding to one or more color values associated with the plurality of objects the non-editable document, before automatically generating the editable document in accordance with at least one of the identified layout, identified font and selected color scheme.