Mobile Document Validation Using Local OCR and Reference Data
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Solution Overview
Problem
Conventional methods for validating business transaction documents using mobile devices rely on complementary documents and remote resources, leading to increased time and cost due to the need for storage, indexing, and computational resources, and are inefficient in extracting data from images with poor quality or complex backgrounds.
Innovation Solution
A method that captures images of documents using a mobile device, performs optical character recognition (OCR), and validates the extracted data against reference information stored locally on the device, allowing for authentication and validation without relying on external resources, and utilizes business rules and complementary documents to improve data extraction and normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If document validation uses complementary documents and remote resources, then validation accuracy is improved, but time consumption and computational resources increase
Solution Approach 1:
The system pre-processes and stores validation rules, reference data, and complementary document templates locally on the mobile device before actual validation occurs. This preliminary preparation enables rapid validation without real-time network access, resolving the contradiction between high validation accuracy and reduced time consumption.
Solution Approach 2:
The system creates local copies of validation resources (business rules, reference information, document templates) on the mobile device, eliminating the need for continuous remote resource access. This copying approach maintains validation accuracy while significantly reducing dependency on external resources and network connectivity.
2Reliability
If document validation uses complementary documents and remote resources, then validation accuracy is improved, but computational resources and storage requirements increase
Solution Approach 1:
Complex computational tasks such as OCR model training, validation rule compilation, and reference data processing are performed in advance on more powerful systems, with only the essential results stored locally. This shifts computational burden from the mobile device to pre-processing systems, maintaining accuracy while reducing on-device resource requirements.
Solution Approach 2:
The system extracts only the essential validation rules and reference information needed for validation, separating critical data from unnecessary computational overhead. This extraction approach reduces storage requirements and computational complexity while preserving the core validation functionality.
3Measurement precision
If conventional image processing techniques are used to improve image quality, then data extraction capability is improved, but additional processing time and complexity are introduced
Solution Approach 1:
The OCR system is trained and optimized to handle various image quality issues directly, enabling the mobile device to perform self-correction of common capture problems without requiring complex post-processing algorithms. This self-service approach improves extraction accuracy while minimizing additional processing complexity.
4Reliability
If document validation requires manual or image-based data entry of reference information, then validation reliability is maintained, but user effort and processing time increase
Solution Approach 1:
The system automatically copies reference information from previously validated documents or stored databases into the current validation process, eliminating the need for manual re-entry. This copying mechanism maintains validation reliability while significantly reducing user effort and processing time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient and accurate automatic validation of business transactions by reducing reliance on external resources, improving data extraction from poor-quality images, and adapting to specific sender formats, thus enhancing the reliability and speed of transaction processing.
Implementation Method 1
capturing an image of a document using a camera of a mobile device
Implementation Method 2
performing optical character recognition (OCR) on the image of the document
Data Source
AI summary
In one approach, a method includes: capturing an image of a document using a camera of a mobile device; performing optical character recognition (OCR) on the image of the document; extracting data of interest from the image based at least in part on the OCR; and validating the extracted data of interest against reference information stored on the mobile device. In another embodiment, a method includes: capturing an image of a document using a camera of a mobile device; performing optical character recognition (OCR) on the image of the document; extracting data of interest from the image based at least in part on the OCR; and validating authenticity of the document based on comparing some or all of the extracted data of interest to reference information stored on the mobile device.