Form Processing System Using ML for Unknown Format Data Extraction
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Solution Overview
Problem
Manual analysis of filled-out forms is time-consuming and prone to errors, and using templates for data extraction becomes unsustainable as the number of forms and updates increases, requiring significant resources and overhead.
Innovation Solution
A form processing and analysis system (FPAS) that uses machine learning and neural network technology to identify and extract data from forms with unknown formats, languages, and layouts by analyzing optical cues and object detection, eliminating the need for manual processing and individual templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of filled-out forms is used, then data extraction can be performed, but the process is time-consuming and prone to errors
Solution Approach 1:
The patent replaces the mechanical manual analysis process with an automated computer-based system that uses optical character recognition (OCR), machine learning models, and natural language processing to extract data from forms. This substitution eliminates human error and significantly reduces processing time while maintaining or improving extraction accuracy.
Solution Approach 2:
The system enables forms to be processed automatically without human intervention. The computer system self-services by receiving form images, automatically detecting form structure and fields, extracting data using OCR and ML algorithms, and outputting structured results. This self-service capability resolves the contradiction by providing both speed and accuracy through automation.
2Reliability
If templates are used for data extraction, then reliability increases, but creating and maintaining templates for each form becomes time-consuming and resource-intensive
Solution Approach 1:
The patent creates a universal form processing system that can handle multiple different form types without requiring separate templates for each. The system uses ML models trained on diverse form data that can adapt to various form layouts, fields, and structures. This multi-functionality maintains reliability across different forms while eliminating the complexity of creating and maintaining individual templates for each form type.
Solution Approach 2:
The system dynamically adapts to different form structures rather than relying on static templates. The ML models can learn and adjust to varying form layouts, field positions, and data patterns. This dynamic capability provides reliable data extraction across diverse forms without requiring pre-defined templates, thereby reducing maintenance overhead while preserving reliability.
3Reliability
If templates are used for every form, then data extraction consistency improves, but the computing and resource overhead increases and becomes unsustainable
Solution Approach 1:
The patent merges the functionality of multiple individual form-specific templates into a single unified ML-based processing system. Instead of maintaining separate templates for each form type, the system combines them into one adaptive model that can handle various form structures. This merging reduces resource overhead and computing requirements while maintaining extraction consistency through the unified approach.
Solution Approach 2:
The system changes from using fixed template parameters for each form to using dynamic parameter adjustment through ML inference. The ML models can adapt parameters such as field detection thresholds, OCR confidence levels, and data validation rules based on the specific form being processed. This parameter flexibility maintains consistency across different forms while optimizing resource usage and avoiding the overhead of managing multiple rigid templates.
Data Source
AI summary
Disclosed herein are various embodiments for an augmented reality interaction, modeling, and annotation system. An embodiment operates by receiving an image including unknown data in an unknown format, including pixels. Each of the pixels is classified as one of a background pixel, a key pixel, or a value pixel representing the unknown data. For a plurality of the pixels classified as key pixels or value pixels, a plurality of locational data values associated with the unknown format are generated. Based on the locational data values, a key image and a corresponding value image from the received image are identified. The key image and the corresponding value image are output.