Neural Network Document Type Classification for Automated Onboarding
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Solution Overview
Problem
Manual review of uploaded document images for identification and verification is time-consuming, error-prone, and delays the onboarding process, especially when image quality is poor or documents are not supported by the system.
Innovation Solution
A system using a single-pass neural network to detect documents and a residual neural network to determine document types, enabling automatic extraction of information using computer vision techniques and optical character recognition, with integrated tools for image preprocessing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review is used to verify uploaded documents, then verification accuracy can be maintained, but processing time increases significantly and error rates increase
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated computer vision system using neural networks. The system employs a residual neural network to detect document types and a single-pass neural network to extract information, substituting human operators with automated algorithms that process documents rapidly without sacrificing verification accuracy.
Solution Approach 2:
The system enables self-service by automatically performing document verification without requiring human intervention. The automated extraction and verification processes allow the system to serve itself, processing uploaded documents through multiple neural network stages including quality assessment, document type detection, and information extraction, thereby eliminating the need for manual review while maintaining reliability.
2Reliability
If manual review is used to assess document quality, then quality control can be maintained, but feedback time to users increases
Solution Approach 1:
The patent replaces manual quality assessment with automated image quality evaluation using neural networks. The system automatically assesses document image quality, detects document types, and provides immediate feedback to users about whether their uploads meet quality standards, eliminating the time delay associated with manual review while maintaining rigorous quality control through algorithmic assessment criteria.
3Reliability
If operators manually extract information from documents, then data accuracy can be verified, but productivity decreases
Solution Approach 1:
The patent replaces manual information extraction with automated optical character recognition (OCR) and neural network-based extraction. The system uses a single-pass neural network to extract information from detected documents and a residual neural network to verify document types, enabling rapid processing of multiple documents simultaneously while maintaining data accuracy through automated verification mechanisms.
Solution Approach 2:
The system performs preliminary document type detection and quality assessment before information extraction. By first using the residual neural network to identify document types and the single-pass neural network to locate relevant information areas, the system prepares the extraction process in advance, improving both accuracy and throughput by avoiding unnecessary processing of irrelevant document sections.
Data Source
AI summary
Techniques are disclosed relating to determining whether document objects included in an image correspond to known document types. In some embodiments, a computing system maintains information specifying a set of known document types. In some embodiments, the computing system receives an image that includes objects. In some embodiments, the computing system analyzes, using a first neural network, the image to identify a document object and location information specifying a location of the document object within the image. In some embodiments, the computing system determines, using a second neural network, whether the document object within the image corresponds to a document type specified in the set of known document types, where the determining is performed based on the location information of the document object. In some embodiments, disclosed techniques may assist in automatically extracting information from documents, which in turn may advantageously decrease processing time for onboarding new customers.