Form Template Matching Using One-Shot Filtering and Pixel Alignment
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Solution Overview
Problem
Existing computer vision systems struggle to efficiently identify and digitize paper forms due to variations in form templates, leading to computationally expensive and inefficient processing, especially when dealing with hundreds or thousands of different forms.
Innovation Solution
A two-step approach using a one-shot neural network to filter down potential templates and a precise pixel comparison method to identify the correct form template, reducing computational complexity by pre-processing with a neural network and then using key point analysis for precise matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If precise pixel comparison is used to identify form templates, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by using a neural network to pre-process and filter form images before performing precise pixel comparison. The neural network performs initial classification and reduces the search space of candidate templates, so that the computationally intensive pixel-by-pixel comparison is only applied to a small subset of likely matches rather than all possible templates.
Solution Approach 2:
The patent segments the form identification process into two distinct stages: (1) neural network-based preliminary classification to narrow down candidate templates, and (2) precise pixel comparison for final verification. This segmentation allows each stage to be optimized independently, reducing overall computational complexity while maintaining high accuracy.
2Reliability
If comprehensive form template matching is performed, then reliability is improved, but productivity decreases
Solution Approach 1:
The neural network performs preliminary filtering to identify only the most relevant candidate templates before the precise matching stage. This preliminary action ensures that comprehensive matching is applied only where necessary, maintaining high reliability while improving processing speed by avoiding unnecessary comparisons with clearly unrelated templates.
Solution Approach 2:
The patent applies partial action by performing precise pixel comparison only on a subset of candidate templates selected by the neural network, rather than exhaustively comparing against all possible templates. This partial application of the computationally intensive matching process maintains sufficient reliability for accurate identification while significantly improving productivity.
3Loss of time
If neural network pre-processing is applied, then processing time is reduced, but use of energy increases
Solution Approach 1:
The neural network performs preliminary filtering to rapidly eliminate non-matching templates before the energy-intensive precise pixel comparison stage. Although the neural network consumes computational energy, it is designed to be more energy-efficient than exhaustive pixel comparison, and its ability to prune the search space results in net energy savings while significantly reducing processing time.
Solution Approach 2:
The patent changes the parameter of comparison from exhaustive pixel-by-pixel analysis of all templates to a two-stage process where the neural network uses learned features for rapid initial filtering. This parameter change in the matching approach reduces overall processing time and can be optimized to balance energy consumption between the neural network stage and the precise matching stage.
Data Source
AI summary
Disclosed are a method and apparatus for determining a given template of a form used by a filled in instance of that type of form from amongst a great number of form templates (a hundred or more). The given instance is evaluated by a neural network that has been trained by a single example of each template in order to reduce the total number of templates down to a manageable amount. Given a list of closest matching templates, the instance is aligned to each of the closest matching templates. The comparison generates a match score. The form template having the greatest match score is the correct form template. Filtering the instance through a one-shot learning neural network before performing a precise comparison enables the process to scale to any number of template forms.