Image Source Classification for Document Processing
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Solution Overview
Problem
Existing image processing techniques struggle to efficiently classify and preprocess images from diverse sources like scanned, photographed, and synthetic documents, leading to suboptimal or excessive preprocessing, which affects the accuracy of text and object recognition tasks.
Innovation Solution
A two-stage classification process using metadata-based classification followed by neural network-based analysis of global and local image features to identify the source type, allowing for customized preprocessing techniques to be applied, optimizing the application of computer vision algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single uniform preprocessing approach is applied to all image types, then the processing pipeline is simple, but the recognition accuracy deteriorates due to suboptimal preprocessing for specific source types
Solution Approach 1:
The patent segments the image processing pipeline by first classifying images into different source types (scanned, photographed, synthetic) and then applying source-type-specific preprocessing operations. This segmentation allows each image type to receive optimized preprocessing treatment, improving recognition accuracy while maintaining manageable complexity through modular processing stages.
Solution Approach 2:
The patent implements dynamic preprocessing by selecting different preprocessing operations based on the detected source type of each image. The system adapts its processing approach in real-time according to the image characteristics, rather than applying a static uniform preprocessing pipeline to all images.
2Reliability
If comprehensive preprocessing is applied to all images, then processing quality improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial preprocessing by selecting only the necessary preprocessing operations based on the image source type classification. Instead of applying all possible preprocessing operations to every image, the system applies only the subset needed for each specific image type, reducing unnecessary computational overhead and processing time while maintaining processing quality.
3Productivity
If source type classification is implemented, then preprocessing optimization is achieved, but system complexity increases
Solution Approach 1:
The patent performs preliminary classification of images into source types before applying preprocessing operations. This preliminary action enables the system to optimize subsequent preprocessing steps based on the classification result, improving processing efficiency while keeping the overall system complexity manageable through a clear two-stage architecture.
Data Source
AI summary
Aspects and implementations provide for techniques of classifying images by source types for efficient, fast, and economical processing of such images. The disclosed techniques include, for example, obtaining an input into an image processing operation (IPO input). The techniques further include processing, using a first neural network (NN), a first image associated with the IPO input to obtain a first feature vector, and processing, using a second NN, a plurality of second images associated with the IPO input to obtain a second feature vector. The techniques further include identifying, using the first feature vector and the second feature vector, a type of source used to generate the IPO input.