Document Image Legibility via Edge Detection and Local Contrast
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for preserving and enhancing the legibility of documents, especially those degraded over time or with poor initial quality, are inadequate in maintaining clarity and readability.
Innovation Solution
A computer-based method that converts images into grayscale, isolates pixels near edges, computes local color contrast, and combines images to produce a more legible black and white output, removing stray pixels and correcting plateaus, thereby enhancing document image clarity without requiring training data or document models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional preservation techniques are used on degraded documents, then the document can be stored and archived, but the legibility and clarity of the document deteriorate over time
Solution Approach 1:
The patent creates a digital copy of the degraded document through scanning or imaging, then applies computational enhancement algorithms to the digital copy. This allows the original physical document to be preserved in its degraded state while the enhanced digital version maintains high legibility for reading and analysis.
Solution Approach 2:
The patent transforms the degraded document image through various parameter changes including contrast enhancement, noise filtering, and binarization. These computational transformations adjust the visual parameters of the document image to improve legibility without altering the original physical document.
2Loss of information
If image enhancement algorithms are applied to improve legibility, then document clarity improves, but the complexity of the processing system increases
Solution Approach 1:
The patent divides the image enhancement process into distinct sequential stages: preprocessing (noise reduction, contrast adjustment), edge detection, binarization, and post-processing. Each stage addresses specific aspects of legibility improvement, making the overall complex system manageable through modular segmentation of functions.
Solution Approach 2:
The patent employs algorithms that automatically adapt to the characteristics of each input document without requiring manual parameter tuning or training data. The system self-adjusts enhancement parameters based on the specific degradation patterns detected in each document, reducing the need for complex user configuration.
3Loss of information
If multiple processing operations are performed to enhance legibility, then the quality of the output image improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary preprocessing operations such as noise reduction and contrast enhancement before applying more computationally intensive algorithms like edge detection and binarization. This preliminary action prepares the image data in a way that accelerates subsequent processing steps and improves overall efficiency.
Solution Approach 2:
The patent applies different processing operations selectively to different regions of the document based on local characteristics. For example, edge detection is applied primarily to text regions rather than uniform background areas, and processing parameters are adjusted locally to match the specific degradation patterns in each region, reducing unnecessary computation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method effectively improves the legibility of degraded documents by isolating dark and light pixels, enhancing sharpness, and removing noise, achieving results comparable to or surpassing state-of-the-art systems in document image binarization contests with reduced variability and failures.
Implementation Method 1
illuminating the document using a plurality of monochromatic light sources having different wavelengths
Implementation Method 2
capture different greyscale images of the document
Data Source
AI summary
A system and method are described for enhancing readability of document images by operating on each document individually. Monochromatic light sources operating at different wavelengths of light can be used to obtain greyscale images. The greyscale images can then be used in any desired image enhancement algorithm. In one example algorithm, an automated method removes image background noise and improves sharpness of the scripts and characters using edge detection and local color contrast computation.