Memory-Efficient Gradient Descriptors for Identity Document Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing document recognition systems face challenges in document localization and classification due to high computational requirements and memory consumption, particularly in training high-accuracy algorithms that require large datasets and millions of parameters, which are difficult to obtain due to regulatory and privacy constraints.
Innovation Solution
A method for constructing memory-efficient, highly discriminative local keypoint or feature descriptors using gradient maps and binary classifiers, which extracts patches from document images, applies classifiers to gradient orientations, and generates feature descriptors for localization and classification, while selecting classifiers to optimize memory usage and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning algorithms are used for document classification, then classification accuracy is improved, but memory consumption and computational time increase significantly
Solution Approach 1:
The patent extracts only the essential gradient orientation information at keypoints, discarding redundant pixel data. By taking out only the necessary gradient map and classifier information, the system achieves accurate document classification with minimal memory consumption, avoiding the need to store large training datasets or complex neural network parameters.
Solution Approach 2:
The patent uses simple binary classifiers that can be easily stored and discarded, replacing complex deep learning models. These lightweight classifiers consume minimal memory while providing sufficient accuracy for document classification, making the system suitable for resource-constrained environments.
2Measurement precision
If deep learning algorithms are used for document classification, then classification accuracy is improved, but training data requirements increase
Solution Approach 1:
The patent employs simple binary classifiers that can be trained with minimal data. Instead of requiring tens of thousands of training samples for deep learning models, the system uses a small set of gradient orientation patterns to train classifiers that achieve high accuracy, significantly reducing data collection requirements.
Solution Approach 2:
The patent changes the representation parameters from raw pixel data to gradient orientation histograms at keypoints. This parameter transformation reduces the dimensionality and complexity of training data requirements, enabling accurate classification with much smaller datasets compared to traditional deep learning approaches.
3Measurement precision
If more classifiers are applied to gradient maps, then recognition accuracy is improved, but computational time and memory usage increase
Solution Approach 1:
The patent applies a limited number of binary classifiers to the gradient map, using only the necessary classifiers to achieve accurate document classification. By using partial action with a small set of well-chosen classifiers rather than exhaustive classification, the system maintains high accuracy while optimizing computational efficiency.
Data Source
AI summary
Memory-efficient feature descriptors for localization and classification of identity documents. In an embodiment, patches are extracted from an input image of a document. For each of the patches, a gradient map is constructed for a plurality of gradient orientations, a plurality of classifiers are applied to rectangles in the gradient map, and a feature descriptor is generated based on the values output by the plurality of classifiers. The feature descriptors are then compared to templates to match the document to one of the templates for document localization and classification.