Automated Image Anchor Template Selection for Document Data Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for extracting data from documents rely heavily on manual selection of image anchor templates, which can lead to sub-par results and resource wastage due to operator skill dependence and the difficulty in predicting template matches across multiple documents, especially with distortions introduced by printing, faxing, and scanning.
Innovation Solution
A method and system for automatically generating image anchor templates using seed templates and exemplars, where candidate templates are ranked based on quality scores for their ability to predict data field locations, and the most highly ranked templates are selected for data extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual selection of image anchor templates is used, then operator flexibility and adaptability are maintained, but operator skill dependence increases and time consumption increases
Solution Approach 1:
The system performs self-service by automatically generating image anchor templates through computational algorithms that analyze document images and identify suitable templates without human intervention. The computer processor executes code to evaluate candidate templates based on quality metrics, eliminating the need for manual operator selection while maintaining adaptability through automated quality assessment
Solution Approach 2:
The manual mechanical process of operator selection is replaced with an automated computational system. The computer processor substitutes human operators by executing algorithms that generate, evaluate, and rank image anchor templates based on quantitative quality scores, transforming a manual task into an automated computational process
2Ease of operation
If manual selection of image anchor templates is used, then operator intuition can be applied, but reliability decreases due to difficulty in predicting template matches
Solution Approach 1:
The system implements feedback by evaluating candidate image anchor templates using quality scores that measure their ability to predict data field locations. The computer processor calculates these scores based on how well templates match across multiple documents, providing quantitative feedback that guides template selection and ensures reliable, consistent results
Solution Approach 2:
The system changes parameters by using computational metrics to evaluate template quality instead of relying on subjective operator judgment. The quality score parameter quantifies template reliability by measuring match consistency across documents, transforming the selection criterion from intuitive to measurable and reliable
3Manufacturing precision
If trial and error process is used by operators, then template selection can be refined, but resource wastage increases and productivity decreases
Solution Approach 1:
The system performs preliminary action by automatically generating and evaluating multiple candidate image anchor templates before final selection. The computer processor pre-computes quality scores for candidate templates, identifying the best matches in advance without requiring iterative trial-and-error refinement by operators, thus improving both precision and productivity
Solution Approach 2:
The system creates multiple copies of candidate templates from the document images and evaluates each copy's quality independently. The computer processor generates several potential image anchor templates and ranks them based on quality metrics, eliminating the need for repeated manual trial-and-error attempts and enabling parallel evaluation that boosts productivity
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
Methods, and corresponding systems, of generating one or more image anchor templates for extracting data from a data field of a first class of documents are provided. The methods include generating (202) one or more candidate image anchor templates from at least one of one or more exemplars of the first class; determining (204) a quality score for each of the one or more candidate image anchor templates using a computer processor and known locations of the data field within the one or more exemplars of the first class; ranking (206) the one or more candidate image anchor templates according to quality score; and selecting (208) one or more of the most highly ranked image anchor templates.