Digital File Recognition Plugin for Automated Document Segmentation
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Solution Overview
Problem
The challenge lies in accurately identifying and depositing digital files, especially those containing multiple documents or images, as they often arrive unidentified, leading to potential loss of vital information due to incorrect identification and labeling.
Innovation Solution
A digital file recognition and deposit system, comprising an email plugin with computer executable instructions that identifies target clients, segments and labels files by matching document templates, and deposits them into appropriate accounts, utilizing optical character recognition (OCR) and document template matching to create identifiers and group pages into unique document images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital files are received via email attachment without identification, then the transmission speed and efficiency are improved, but the accuracy of file identification and deposit is deteriorated
Solution Approach 1:
The system performs preliminary actions by automatically analyzing email attachments upon receipt, extracting document images, comparing them against templates, and pre-identifying file types before manual processing. This preliminary automated classification enables rapid bulk handling while maintaining accuracy, resolving the contradiction between transmission efficiency and identification precision.
Solution Approach 2:
The system implements self-service through automated optical character recognition (OCR) and template matching algorithms that independently identify and classify documents without human intervention. The email plugin automatically processes attachments, creates identifiers, and deposits files to appropriate accounts, enabling the system to serve itself in the identification task while preserving both speed and accuracy.
2Quantity of substance
If multiple document images are contained in a single unidentified file, then the information density is improved, but the difficulty of accurate identification and labeling is deteriorated
Solution Approach 1:
The system applies segmentation by dividing the unidentified file into individual document images for separate analysis. Each document image is extracted and compared against templates independently, allowing the system to handle multiple documents within a single file while maintaining accurate identification for each segment, thus resolving the contradiction between information density and identification difficulty.
Solution Approach 2:
The system introduces an intermediary template matching mechanism that serves as a mediator between the unidentified file and the classification system. By comparing each extracted document image against a library of templates, the intermediary matching process enables accurate identification of multiple document types within a single file, reducing the complexity of handling high-information-density attachments.
3Productivity
If automated template matching is used to identify document types, then the productivity of file processing is improved, but the device complexity increases
Solution Approach 1:
The system uses copying by creating simplified template representations of common document types and comparing attachments against these templates. This copying approach enables rapid automated matching and identification without requiring complex analysis, thereby improving processing productivity while keeping the template matching mechanism relatively simple and manageable.
Solution Approach 2:
The system applies parameter changes by adjusting the complexity and detail of template matching based on processing needs. The email plugin can modify matching parameters such as similarity thresholds and template granularity, allowing the system to maintain high productivity through automated processing while managing device complexity by adapting the matching parameters to the specific processing context.
Data Source
AI summary
Systems and methods for recognizing and depositing digital files. Receive an unidentified file. Identify a target client and at least one account associated with the unidentified file. Segment the unidentified file into one or more document images. For each document image: scan the image and extract content, label the image based on its content, select an account of the target client, and deposit the labeled image in the selected account.