Document Identification via Image Attribute Analysis
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Solution Overview
Problem
In high-speed check processing systems, issues arise when the image of a check cannot be properly captured, leading to the need for substitute documents or check carriers, which can cause image quality issues and require separate handling due to pricing and clearing rules.
Innovation Solution
The system identifies document types by analyzing image attributes, such as document size, using tagged image file format (TIFF) tags, and compares these attributes to stored criteria like MICR data to determine if a document is a check in a carrier or a paper check, enabling accurate processing and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-speed check processing is implemented, then processing speed and productivity are improved, but image capture reliability deteriorates leading to improper image capture
Solution Approach 1:
The system performs preliminary analysis of image attributes (such as checking for carrier document characteristics) before final processing decisions are made. This allows the system to identify potential image capture issues early in the high-speed processing flow, enabling corrective actions or alternative processing paths to be taken before problems propagate through the system.
2Productivity
If substitute documents or check carriers are used, then document processing continuity is maintained, but image quality deteriorates and requires separate handling
Solution Approach 1:
The system segments the document processing flow by identifying different document types (regular checks versus carrier documents) through image attribute analysis. This segmentation allows the system to apply different processing rules and quality standards to different document types, maintaining overall processing continuity while appropriately handling the specific quality characteristics of substitute documents.
Solution Approach 2:
The image attribute analysis system acts as an intermediary between the high-speed capture system and the final processing system. It analyzes intermediate attributes of captured images to identify carrier documents and substitute documents, enabling the main system to adjust its processing accordingly without requiring complete re-processing or manual intervention.
3Measurement precision
If document type identification is added to the processing system, then processing accuracy is improved, but system complexity increases
Solution Approach 1:
The system uses the existing image capture infrastructure to perform document type identification by analyzing attributes already present in the captured images. Rather than adding separate sensing systems or complex identification hardware, the system leverages the self-contained information in the image data itself (such as size attributes, format characteristics) to automatically determine document type, thereby improving accuracy without proportionally increasing system complexity.
Data Source
AI summary
Stored documents can be recognized and identified by evaluation of their attributes from an image file to obtain a document specification that can be compared to stored data about the document. The image file is analyzed using a processor in order to discern image attributes. The image attributes form a specification for the document in the image. This specification can then be compared to the stored criteria using the processor, and the result of the comparison can be stored. An embodiment of the invention can be used in a check processing system for a financial institution. In such a case, the corresponding data comprises MICR data and the image attributes can, for example, indicate whether the document is a check in a carrier, as opposed a paper check by itself.