Dynamic Neural Network Selection for Image Data Recognition
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Solution Overview
Problem
Conventional image processing techniques fail to accurately recognize data in images with multiple background variations, as they are typically suited for specific background types and cannot dynamically adapt to varying scenarios.
Innovation Solution
A method and system that segment images to identify objects, generate object relationship data with confidence scores, and use a knowledge base representation to train a Recurrent Neural Network (RNN) for dynamic selection of the most appropriate neural network for data prediction and recognition, enabling accurate text/object identification across different background variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single image processing approach is used, then the system is simple and easy to implement, but it cannot accurately recognize data in images with varying background variations
Solution Approach 1:
The system dynamically selects the most appropriate neural network model based on the characteristics of the input image, such as background color and text type. This dynamic selection mechanism allows the system to adapt to varying background variations without requiring a fixed, complex multi-model architecture for every possible scenario.
Solution Approach 2:
The system changes the parameters of the neural network model based on the image characteristics. By analyzing the background color and other features of the input image, the system selects or configures the appropriate model parameters to optimize recognition accuracy for that specific scenario.
2Measurement precision
If multiple neural network models are maintained for different background variations, then recognition accuracy improves, but the system complexity and computational overhead increase
Solution Approach 1:
Instead of maintaining multiple static models ready for deployment, the system uses a dynamic selection approach where a single neural network is configured with different parameters based on the input image characteristics. This reduces the computational overhead of loading and switching between multiple pre-trained models while maintaining recognition accuracy.
Solution Approach 2:
The system achieves multiple model capabilities by changing the parameters of a single neural network based on the detected background variations. This parameter-based adaptation allows the system to handle different background types without the computational cost of maintaining separate trained models for each scenario.
3Measurement precision
If conventional image processing techniques are used, then the system is computationally efficient, but it fails to accurately determine data in images with multiple background variations
Solution Approach 1:
The neural network system dynamically adapts to different background variations by selecting or configuring appropriate model parameters based on the input image characteristics. This dynamic adaptability allows the system to handle diverse background types (white, colored, patterned) that conventional static image processing techniques cannot accommodate.
Solution Approach 2:
The system changes the neural network parameters based on the background characteristics detected in the input image. This parameter adaptation enables the system to maintain high recognition accuracy across different background variations, overcoming the limitation of conventional techniques that are optimized for specific background types only.
Data Source
AI summary
The present disclosure relates to a method and system for recognition of data in one or more images. The method receives and segments the one or more images to identify segmented objects. Further, the method generates an object relationship data for each of the segmented objects and determines a knowledge base representation of the object relationship data based on defined features. Furthermore, a Recurrent Neural Network (RNN) is trained based on the knowledge base representation to determine an appropriate Neural Network (NN) having optimum confidence score. Based on the appropriate NN selected, the objects in the input image is predicted and transmitted to external systems for decision making. Thus, enabling accurate text/object identification for the images having different background variations using the dynamic selection of NN, thereby facilitating more effective decision making.