ML Blur Detection and Image Stitching for Text Image Correction
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Solution Overview
Problem
Existing artificial intelligence systems struggle with identifying and correcting blurs in images due to the lack of a framework to determine blur location and cause, inability to determine appropriate responses, and lack of automatic correction methods, leading to inefficient and potentially insecure manual review processes.
Innovation Solution
A system using machine learning models to identify blur types, apply filters or stitching techniques to correct blurs, and request additional images when necessary, ensuring efficient, automatic, and accurate blur identification and rectification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review is used to assess blurred images, then accuracy in determining whether blur affects text extraction can be maintained, but time consumption and cost increase significantly
Solution Approach 1:
An automated blur detection system acts as an intermediary between image input and text extraction processing. This system analyzes images for blur characteristics and generates recommendations, serving as a mediator that reduces the need for manual review while maintaining assessment accuracy. The intermediary process automatically evaluates blur severity and determines whether images require manual inspection, thereby reducing time consumption.
Solution Approach 2:
The manual mechanical review process is replaced with an automated computational system that uses image analysis algorithms to detect and assess blur. This substitution eliminates the need for human reviewers to manually examine each image, dramatically reducing time consumption while maintaining or improving assessment consistency and accuracy.
2Reliability
If manual review is used for blurred images, then security risks from confidential data exposure can be identified, but the process becomes inefficient and costly
Solution Approach 1:
The automated blur detection system serves as a security intermediary by identifying images that require review and flagging potential security concerns. It filters and prioritizes images based on blur characteristics, ensuring that only necessary cases undergo manual security review, thereby maintaining reliability while improving productivity.
Solution Approach 2:
The system performs self-assessment of image quality and automatically determines security implications without requiring manual intervention for every image. It autonomously evaluates blur severity, assesses potential impact on text extraction, and makes recommendations, enabling the system to serve itself and improving overall processing efficiency.
3Productivity
If automated blur detection is implemented, then processing speed and efficiency improve, but the system lacks the ability to accurately assess blur impact on text extraction
Solution Approach 1:
The automated system incorporates feedback mechanisms where blur detection results are continuously refined based on performance data and comparison with manual review outcomes. The system learns from feedback to improve its blur assessment algorithms, enabling it to maintain high processing speed while progressively improving assessment accuracy through iterative optimization.
Solution Approach 2:
The system replaces manual assessment mechanics with sophisticated automated image analysis algorithms that can process images rapidly. These computational mechanisms use pattern recognition and machine learning to assess blur impact on text extraction with accuracy comparable to or exceeding manual review, while maintaining high processing speed.
4Manufacturing precision
If comprehensive blur analysis is performed to identify all blur types and locations, then correction accuracy improves, but system complexity increases
Solution Approach 1:
The blur analysis system is segmented into specialized modules, each responsible for detecting specific blur types (motion blur, defocus blur, etc.) and locations. This segmentation allows the system to achieve comprehensive analysis accuracy while managing complexity through modular design, where each module can be independently optimized and maintained.
Solution Approach 2:
The system applies different analysis methods and levels of detail to different regions of images based on local characteristics. It focuses computational resources on areas with blur issues rather than uniformly analyzing entire images, improving correction accuracy where needed while reducing overall system complexity and computational burden.
Data Source
AI summary
Methods and systems are described herein for identifying the location and nature of any blur within one or more images received as a user communication and generating an appropriate correction. The system utilizes a first machine learning model, which is trained to identify blurred components of inputted images and determine whether the blurred components are located in portions of the inputted images comprising textual information. The system may apply a corrective action selected by the first machine learning model, which may comprise stitching blurred images together to a sharp product image and/or some other method appropriate for rectifying images received.