Machine Learning Document Image Extraction for Variable Forms
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Solution Overview
Problem
Existing systems fail to efficiently extract customer information from forms with unknown or variable structures, low image quality, and diverse capture methods, particularly with the increasing use of mobile devices for data gathering.
Innovation Solution
A document recognition and extraction system utilizing machine learning techniques to analyze document images, correcting for rotation, performing text segmentation, and recognizing text, even when the form structure is unknown, and the image quality is poor, allowing for flexible and accurate extraction of key-value pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a tabular or structural definition is not known about a form before it is captured, then the system cannot be pre-programmed to recognize the form structure, but this causes the image capture and extraction system to fail to capture customer information from the form
Solution Approach 1:
The patent segments the form processing task into multiple independent components: form detection, field identification, data extraction, and validation. Each component handles specific aspects of form processing independently, allowing the system to adapt to various form structures without requiring complete pre-programming of the entire form layout.
Solution Approach 2:
The system employs self-service mechanisms where the extraction process automatically adapts to the form structure by detecting fields and their types dynamically. The system performs self-validation and self-correction during the extraction process, eliminating the need for manual pre-programming of form-specific logic.
2Ease of operation
If images of forms are captured by agents using varied techniques with mobile devices, then data gathering becomes convenient and accessible, but this results in captured images being skewed, having low and/or differing resolutions, and varying quality
Solution Approach 1:
The patent applies preliminary actions by performing image preprocessing operations such as skew correction, resolution normalization, and quality enhancement before the actual data extraction process. These preliminary steps prepare the varied-quality images from mobile devices into a consistent format suitable for reliable extraction.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the input image quality characteristics. For images with different resolutions, skew angles, and quality levels, the extraction algorithm automatically modifies its parameters to optimize performance, transforming the extraction process to match the specific characteristics of each captured image.
3Manufacturing precision
If specialized scanners or high resolution multi-function peripheral devices are used to capture form images with controlled techniques, then consistent and high resolution images are generated, but this reduces flexibility and fails when form formats change or are unknown
Solution Approach 1:
The patent implements dynamic adaptability where the extraction system continuously adjusts its behavior based on the detected form structure. Rather than relying on fixed, pre-programmed form layouts, the system dynamically identifies field positions, types, and relationships during the extraction process, allowing it to handle both known and unknown form formats effectively.
Solution Approach 2:
The extraction system is designed with universal capabilities to handle multiple form types and structures through a single unified approach. The system can process various form formats (known and unknown) using the same core extraction mechanisms, eliminating the need for separate processing paths for different form types.
Data Source
AI summary
A method and apparatus for recognizing and extracting data from a form depicted within an image of a document are described. The method may include receiving the image of the document, the image depicting the form and data contained one the form. The method may also include transforming the image of the document to a set of one or more key, value pairs by processing the image of the document with a sequence of two or more trained machine learning based image analysis processes, wherein keys are relevant to forms of the type depicted in the form, and wherein each value is associated with a key. The method may also include generating a data output that comprises the set of key, value pairs for textual data recognized and extracted from the form depicted in the image.