Document Upload Type Detection Using Dual OCR Verification
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Solution Overview
Problem
Existing document upload processes in computer systems fail to accurately and efficiently identify document types during the upload process, particularly in systems that require specific document types for processing, leading to inefficiencies and inaccuracies.
Innovation Solution
A two-step optical character recognition (OCR) process is implemented, using a keyword recognition engine and an account character recognition engine to identify and verify document types, with the backend system initiating detection during the upload process, allowing for real-time validation of document types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single character recognition engine is used to detect document type, then the device complexity is reduced, but the measurement precision of document type identification deteriorates
Solution Approach 1:
The character recognition system is segmented into two specialized engines: a keyword recognition engine for identifying document type keywords and an account character recognition engine for verifying account information. This segmentation allows each engine to specialize in specific recognition tasks, improving overall identification accuracy while maintaining manageable system complexity through modular design.
2Ease of operation
If document type detection is performed after complete upload, then the ease of operation is improved, but the loss of time is increased
Solution Approach 1:
The system performs preliminary document type detection during the upload process itself, rather than after complete upload. The backend system initiates detection operations on uploaded documents in real-time, enabling early identification of unsupported document types and allowing the system to cancel uploads before they complete, thereby reducing wasted time and resources.
3Reliability
If multiple character recognition engines are used, then the reliability of document type detection is improved, but the device complexity increases
Solution Approach 1:
The reliable multi-engine system is achieved through segmentation into specialized engines with distinct functions: keyword recognition for document type classification and account character recognition for verification. This modular segmentation improves reliability through functional specialization while controlling complexity through clear separation of concerns and defined interfaces between engines.
Solution Approach 2:
The system implements feedback mechanisms where the account character recognition engine verifies the results of keyword recognition. The backend system coordinates between engines, using feedback from account character verification to confirm or correct document type predictions, thereby enhancing reliability through cross-validation while managing complexity through structured feedback loops.
4Productivity
If real-time document type verification is implemented, then the productivity is improved, but the use of energy is increased
Solution Approach 1:
Real-time verification improves productivity by enabling early cancellation of unsupported document types during upload. The system performs preliminary detection and verification operations while the upload is in progress, allowing quick rejection of invalid documents before full processing occurs, thereby increasing overall throughput while managing energy consumption through selective processing.
Data Source
AI summary
Systems for document type detection during a document upload process are disclosed. Character recognition processes are performed on images of a document being uploaded to a backend system. The character recognition processes may identify different subsets of characters in the document which are used to identify the document type. Subsets of characters may include keywords and account characters and each subset may be processed by a different character recognition process. One or both of the subsets of characters may be used to identify the document type and the identified document type may be used to decide whether to proceed with the document upload process.