Form Document Acceptance Via CNN Signature and Field Detection
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Solution Overview
Problem
Conventional form document review processes struggle to accurately determine the acceptability of documents due to difficulties in distinguishing between wet-ink and digitally generated signatures, and missing required fields, leading to incorrect acceptability determinations.
Innovation Solution
A neural network is trained using labeled images of acceptable and unacceptable form documents to classify the acceptability of new form documents, utilizing a convolutional neural network (CNN) model to identify characteristics such as handwritten signatures and filled fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review processes are used to determine form document acceptability, then measurement precision can be maintained, but productivity is low and time-consuming
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated neural network system. The neural network analyzes form document images to determine acceptability, substituting human visual inspection and judgment with an automated computational system that processes documents rapidly and consistently.
Solution Approach 2:
The patent creates a digital copy of the form document in image format and analyzes this copy using the neural network. Instead of physically handling and manually reviewing the original document, the system creates a digital representation and processes this copy automatically, enabling rapid analysis without touching the physical document.
2Reliability
If conventional review processes are used, then device complexity remains low, but reliability is poor due to incorrect acceptability determinations
Solution Approach 1:
The patent replaces simple conventional review processes with a neural network-based system that uses deep learning models. This substitution improves reliability by enabling the system to accurately distinguish between wet-ink signatures and digitally generated signatures, as well as detect missing required fields, despite the increased computational complexity.
Solution Approach 2:
The patent changes the parameters of the review system by transitioning from manual visual inspection to automated image analysis using neural networks. The system analyzes multiple parameters including signature characteristics, field completion status, and document formatting, enabling more reliable acceptability determinations through comprehensive data analysis.
3Speed
If manual detection methods are used to identify wet-ink signatures, then measurement precision is maintained, but speed is slow and labor-intensive
Solution Approach 1:
The patent replaces manual visual detection of wet-ink signatures with an automated neural network that analyzes signature characteristics from document images. The system automatically identifies wet-ink signatures by detecting specific visual characteristics such as ink flow patterns, pressure variations, and handwriting dynamics, eliminating the need for manual inspection.
Solution Approach 2:
The neural network system performs self-service analysis by automatically detecting signature types and document acceptability without requiring human intervention. The system independently analyzes the document, makes determinations about wet-ink signatures, and provides acceptability assessments autonomously, eliminating the need for manual detection processes.
Data Source
AI summary
The following generally relates to using image classification techniques to determine the acceptability of form documents. In some examples, an image classification model may be trained to apply a first label to form documents that are acceptable and a second label to form documents that are unacceptable. In these examples, the image classification model may include a neural network, such as a convolutional neural network. Accordingly, the systems and methods generally relate to obtaining a submitted form document, inputting the submitted form document into the trained image classification model, and/or enforcing the acceptability decision of the image classification model.