Digital Document Scan Mark Removal with Content-Aware Filters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional digital image editing systems are inefficient and inflexible in removing scan marks from digital documents, requiring excessive user interactions and computing resources, and are often incompatible with devices having limited capabilities.
Innovation Solution
An autonomous mark removal system utilizing content-aware filters and scan mark models to identify and remove various types of scan marks without user interaction, generating mark-specific masks and filling masked regions to produce clean digital documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional digital image editing systems use user interface tools (e.g., digital brush tools) to remove scan marks, then users can manually edit out image artifacts, but the operation becomes inefficient and inflexible requiring excessive user interactions
Solution Approach 1:
The system performs autonomous scan mark removal without requiring user interactions. The content-aware filter automatically identifies scan marks, generates segmentation masks, and fills masked regions to produce cleaned documents, eliminating the need for manual editing operations
Solution Approach 2:
The system pre-processes the digital document by automatically detecting scan marks and generating segmentation masks before the actual cleaning operation. This preliminary identification and masking allows the system to prepare the document for automated repair, reducing the time and effort required for manual editing
2Adaptability or versatility
If conventional digital image editing systems provide user interface tools for removing scan marks, then users can control the editing process, but the device complexity and computing resources required increase
Solution Approach 1:
The patent replaces manual mechanical operations (user interface tools, digital brush tools) with an automated content-aware filter system. The system uses machine learning models and algorithms to automatically identify and remove scan marks, substituting complex user interaction mechanisms with automated computational processes
Solution Approach 2:
The system segments the document processing into distinct automated stages: scan mark detection, segmentation mask generation, and masked region filling. This segmentation allows each component to be optimized independently and reduces overall system complexity by breaking down the complex editing task into manageable automated steps
3Manufacturing precision
If conventional systems require excessive user interactions to remove scan marks, then accuracy can be controlled by user input, but the productivity decreases due to time-consuming operations
Solution Approach 1:
The content-aware filter system incorporates feedback mechanisms where the system continuously refines its scan mark detection and segmentation mask generation based on the document content. This automated feedback loop ensures high precision in scan mark identification while maintaining high productivity by eliminating manual verification steps
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for implementing content-aware filters to autonomously remove scan marks from digital documents. In particular implementations, the disclosed systems utilize a set of targeted scan mark models in a scan mark removal pipeline. For example, each scan mark model includes a corresponding content-aware filter configured to identify document regions that match a designated class of scan marks to filter. Examples of scan mark models include staple scan mark models, punch hole scan mark models, and page turn scan mark models. In certain embodiments, the disclosed systems then use the scan mark models to generate mark-specific masks based on document input features. Additionally, in some embodiments, the disclosed systems combine the mark-specific masks into a final segmentation mask and apply the final segmentation mask to the digital document for correcting the identified regions with scan marks.