Document Image Capture Edge Detection Geometric Correction
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Solution Overview
Problem
Existing technologies face challenges in accurately capturing and processing document images, particularly in handling uneven lighting, skewing, and background contrast, which affects the reliability and efficiency of data extraction from images captured by mobile and desktop devices.
Innovation Solution
The system optimizes image capture by using edge detection techniques, geometric correction, and quality testing processes to enhance image quality before transmission, employing unique weighting and comparative testing methods to improve readability and reduce the impact of background variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional image capture methods are used, then the process is simple, but image quality is poor due to uneven lighting, skewing, and background contrast
Solution Approach 1:
The system performs edge detection, geometric correction, and quality testing locally on the mobile device before transmission to the server. This preliminary processing ensures image quality is optimized at the source, eliminating the need for complex server-side processing and reducing transmission time while maintaining high reliability.
Solution Approach 2:
The image processing is divided into distinct modules: edge detection module, geometric correction module, quality testing module, and data extraction module. Each module handles a specific aspect of image processing independently, making the overall complex task manageable and efficient through modular architecture.
2Measurement precision
If multiple quality tests are performed at multiple pixel densities, then reading accuracy is improved, but processing time increases
Solution Approach 1:
The system performs quality tests at multiple pixel densities locally on the mobile device before transmission. By completing these time-consuming tests in advance on the device, the system eliminates transmission delays and ensures high reading accuracy without increasing overall processing time, as the tests are done parallel to image capture rather than sequentially.
Solution Approach 2:
The system provides immediate feedback to the user about image quality and reading accuracy results. This feedback mechanism allows the system to optimize processing based on actual performance metrics, adjusting pixel density testing dynamically to achieve high accuracy while minimizing time loss.
3Reliability
If edge detection and geometric correction are applied, then image readability is improved, but device resources are consumed
Solution Approach 1:
The mobile device performs edge detection and geometric correction autonomously using its own processing capabilities and resources. The device serves itself by completing image optimization locally without requiring additional server resources, thereby improving readability while managing device resource consumption efficiently through native hardware utilization.
Solution Approach 2:
The system dynamically adjusts processing parameters such as pixel density, edge detection sensitivity, and geometric correction intensity based on image characteristics and device capabilities. This adaptive approach optimizes image readability while minimizing unnecessary resource consumption by applying higher processing intensity only when and where needed.
Data Source
AI summary
The present invention relates to the automated processing of documents and, more specifically, to methods and systems for aligning, capturing and processing document images using mobile and desktop devices. In accordance with various embodiments, methods and systems for document image alignment, capture, transmission, and verification are provided such that accurate data capture is optimized. These methods and systems may comprise capturing an image on a mobile or stationary device, analyzing images using iterative and weighting procedures, locating the edges or corners of the document, providing geometric correction of document images, converting the color image into a black and white image, transmitting images to a server, and testing the accuracy of the images captured and transmitted.