Bounding Box Precision Models for Efficient Document Image Processing
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Solution Overview
Problem
Existing digital image processing operations face inefficiencies and reliability issues in determining optimal bounding boxes and classifications, particularly in handling diverse document layouts and resource allocation for post-processing tasks.
Innovation Solution
The use of bounding box precision models to determine optimal kernel sizes for object differentiation, combined with image classification machine learning, to generate precise bounding boxes and classifications, and efficient resource allocation for post-processing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional digital image processing operations are used to determine bounding boxes and classifications, then the processing can be performed with existing methods, but the computational complexity and resource requirements are high, leading to inefficiency
Solution Approach 1:
The patent changes the parameter of kernel size selection from arbitrary or fixed values to optimized values derived from a precision model. The system identifies optimal kernel sizes that maximize bounding box precision while minimizing computational resources, directly resolving the contradiction between processing efficiency and computational complexity.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing precision models that capture the relationship between kernel sizes and bounding box precision. These pre-computed models are then used during actual image processing to quickly determine optimal kernel sizes without performing complex real-time calculations, thereby improving efficiency while reducing runtime computational complexity.
2Quantity of substance
If larger kernel sizes are used for object differentiation, then fewer bounding boxes are generated, but the precision of bounding box detection deteriorates
Solution Approach 1:
The patent implements feedback by using precision models that are trained on ground truth data to evaluate the precision implications of different kernel size choices. The system receives feedback from the precision model about the expected precision outcome of each kernel size option and uses this feedback to select the optimal kernel size that balances quantity and precision requirements.
Solution Approach 2:
The patent replaces the mechanical trial-and-error approach of selecting kernel sizes with an intelligent system based on precision models and machine learning. Instead of relying on fixed rules or exhaustive search, the system uses learned patterns from training data to directly predict optimal kernel sizes, substituting mechanical processes with intelligent computation.
3Measurement precision
If multiple kernel sizes are evaluated to find the optimal one, then bounding box precision improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs the computationally intensive model training and precision evaluation in advance, before actual image processing. The precision models are pre-computed offline and stored for rapid lookup during runtime. This preliminary action transfers the computational burden from the time-critical processing path to an offline setup phase, allowing high precision without real-time performance penalties.
Solution Approach 2:
The patent creates simplified representations (precision models) that capture the essential relationships between kernel sizes and precision outcomes. Instead of re-evaluating multiple kernel sizes during actual processing, the system copies the learned knowledge from training into compact models that can be quickly queried, preserving precision benefits while eliminating repeated computational overhead.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing digital image processing operations. For example, as described herein, various embodiments of the present invention relate to performing digital image processing operations using at least one of using bounding box precision models to generate an optimal object differentiation kernel, using an optimal object differentiation kernel to generate/detect optimal bounding boxes of an image set, and using an image classification machine learning model to generate bounding box classifications for the optimal bounding boxes of an image set.