Scanbox Image Classification Using Edge Angles
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Solution Overview
Problem
The accumulation of mixed images containing documents in digital devices poses a challenge for users to efficiently find and organize specific document images, as existing technologies lack effective image classification and organization methods.
Innovation Solution
The proposed solution involves generating a compact representation of an image using pixel values and determining angle measurements for potential document edges within the image, allowing for automatic classification and organization of images containing documents without user intervention, using techniques like the Hough Transform and machine learning classifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users upload entire sets of captured images to content management systems without pre-review, then storage capacity is utilized efficiently, but image retrieval and organization becomes difficult
Solution Approach 1:
The system performs preliminary classification of images during the upload process by analyzing edge angles and compact representations to identify documents before they are stored. This preliminary action tags images with document-related metadata, enabling efficient later retrieval without requiring users to review images beforehand.
Solution Approach 2:
The patent introduces an intermediary classification system that acts as a mediator between image upload and storage. This system analyzes image features (edge angles, compact representations) and automatically categorizes images containing documents, creating an organizational layer that facilitates retrieval without increasing user effort.
2Ease of operation
If manual review and organization of images is performed before uploading, then image organization is improved, but user time and effort increase
Solution Approach 1:
The system implements self-service automation where the classification algorithm independently analyzes uploaded images using edge detection and compact representation techniques. The system automatically identifies and categorizes document-containing images without requiring user intervention, making the organization process self-sufficient and eliminating time loss for manual review.
Solution Approach 2:
The patent replaces the mechanical process of manual image review with an automated computational system. Machine learning classifiers and image processing algorithms substitute for human users in the organization task, analyzing edge angles and image features to automatically categorize images, thereby eliminating the time users would otherwise spend on manual organization.
3Productivity
If automated image classification is implemented, then image organization efficiency is improved, but computational complexity increases
Solution Approach 1:
The classification system is segmented into distinct functional modules: edge detection component, compact representation generator, angle measurement calculator, and classification decision engine. This segmentation allows each component to perform a specific task efficiently, improving overall productivity while managing complexity through modular design that can be implemented and optimized independently.
Solution Approach 2:
The system applies partial action by focusing computational resources only on images that require classification (those with detected edges), rather than processing every uploaded image uniformly. By calculating angle measurements and compact representations only when necessary, the system achieves high productivity for document detection while reducing overall computational complexity and resource consumption.
Data Source
AI summary
Embodiments are provided for content item classification. In some embodiments, an image for classification is received, a compact representation for the image having values indicative of pixel values within the received image is generated, a plurality of angle measurements for possible edges of at least one potential document within the received image are determined, and the image is classified using said compact representation and said plurality of angle measurements.