Document Image Cleaning Confidence Map for Shadow Removal
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Solution Overview
Problem
Digital images captured using mobile devices often suffer from poor quality due to inconsistent lighting, shadows, and incorrect focus, leading to unclear and difficult-to-read documents, with conventional image processing systems requiring significant computational power and calibrated equipment.
Innovation Solution
The system generates a cleaning confidence map to differentiate between text, background, and graphics in digital images, adjusting pixel luminosity and color based on confidence levels to enhance image quality, removing shadows and correcting contrast without needing calibrated cameras or light sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning classifiers are used to identify and correct dark regions, then image quality improvement is achieved, but computational power requirements increase significantly
Solution Approach 1:
The patent segments the image processing task by dividing it into distinct stages: shadow detection, shadow removal, and contrast correction. Each stage uses specialized algorithms optimized for its specific function, avoiding the need for a single complex machine learning classifier to handle all aspects, thereby reducing overall computational requirements while maintaining image quality improvement
Solution Approach 2:
The patent introduces intermediate processing steps between capture and final output, including shadow detection algorithms and confidence maps that guide subsequent processing. These intermediaries enable targeted corrections without requiring exhaustive computational analysis of the entire image, reducing the computational burden while still achieving reliable quality improvement
2Measurement precision
If complex machine learning classifiers are deployed to enhance digital images, then text and shadow identification improves, but device complexity and computational requirements increase
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on local characteristics. Shadow regions receive shadow removal processing, while text regions receive contrast enhancement. This localized approach improves identification accuracy without requiring a single complex classifier to analyze the entire image, thereby reducing overall system complexity
Solution Approach 2:
The patent modifies image parameters such as luminosity, contrast, and color balance in a controlled sequence rather than using complex machine learning to infer all parameters simultaneously. This parameter-based approach achieves precise text identification while maintaining simpler processing architecture
3Reliability
If aggressive pixel modification is applied to clean background areas, then background quality improves, but text and graphic areas may be distorted
Solution Approach 1:
The patent applies different modification intensities to different image regions based on their content type. Background areas receive aggressive cleaning to remove shadows and improve uniformity, while text and graphic areas receive gentler processing to preserve their structural integrity. This spatially-varying approach resolves the contradiction between background cleanliness and text preservation
Solution Approach 2:
The patent uses confidence maps that provide feedback about the likelihood of each pixel belonging to text, graphics, or background. This feedback mechanism guides the processing intensity applied to each region, preventing over-modification of text areas while enabling thorough cleaning of background areas, thus maintaining both background quality and text integrity
Data Source
AI summary
Systems and methods are disclosed for content aware digital image enhancement. In particular, in one or more embodiments, the disclosed systems and methods analyze content of a digital image portraying a document with graphics and/or text and generate a cleaning confidence map. Specifically, in one or more embodiments, the disclosed systems and methods generate a cleaning confidence map indicating a likelihood that each pixel in the digital image portrays text or a graphic. Moreover, in one or more embodiments, the disclosed systems and methods utilize the cleaning confidence map as a reflection of how aggressively to modify digital images. In particular, in one or more embodiments, the disclosed systems and methods utilize the cleaning confidence map to remove shadows, identify and clean background pixels, and correct contrast in relation to the digital image.