OCR Document Classification With Adaptive Learning Feedback
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Solution Overview
Problem
Existing document classification methods face challenges in efficiently and accurately classifying documents due to high dimensionality, computational complexity, and the need for supervised learning, particularly in unstructured or semi-structured documents, while maintaining privacy and security.
Innovation Solution
A supervised document classification algorithm using alphanumeric strings, combined with a distributed architecture, employs optical character recognition (OCR) and adaptive learning, prompting users for unreliable classifications, and utilizing a cloud-based system for efficient and secure document classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional document classification methods are used, then classification can be performed, but computational complexity and processing time increase significantly due to high dimensionality of document data
Solution Approach 1:
The patent segments the document processing task into multiple stages: pre-processing (OCR, text extraction), feature extraction (n-gram generation), and classification. This segmentation allows each stage to be optimized independently and reduces the computational burden on the classification system by preparing features in advance.
Solution Approach 2:
The system performs preliminary actions by extracting and preprocessing document features before classification. N-gram features are pre-computed and stored, allowing the classification algorithm to work with pre-prepared data rather than processing raw documents in real-time, significantly reducing computational complexity during classification.
2Measurement precision
If supervised learning algorithms are used for document classification, then classification accuracy improves, but the need for labeled training data increases complexity and cost
Solution Approach 1:
The system employs self-service mechanisms where the classification model continuously learns from newly classified documents without requiring manual relabeling. When documents are classified automatically, the system uses these results to refine its model, allowing the system to improve its own performance using its own outputs rather than requiring external annotated data.
Solution Approach 2:
The patent implements feedback loops where classification results are fed back into the training process. The system monitors classification performance and uses misclassified or newly classified documents to adjust and refine the classification algorithm, enabling continuous improvement without requiring additional labeled training data from external sources.
3Adaptability or versatility
If distributed architecture is implemented, then system scalability and privacy are improved, but system complexity and setup difficulty increase
Solution Approach 1:
The system is segmented into distributed components including client systems, servers, and cloud-based services. Each component performs specific functions (document intake, processing, classification) and can be independently configured or scaled, enabling the system to adapt to different organizational needs while maintaining modularity that reduces overall complexity.
Solution Approach 2:
The patent introduces intermediary components such as message queues, API gateways, and standardized data formats that facilitate communication between distributed system elements. These intermediaries abstract the complexity of inter-component communication, allowing the system to scale without requiring direct complex interactions between all components.
4Reliability
If comprehensive document processing is performed, then classification reliability improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by selectively processing document features based on their importance and the specific classification task requirements. Rather than processing all possible document attributes, the system identifies and processes only the most relevant features (such as n-gram patterns) that contribute to reliable classification, reducing processing time while maintaining accuracy.
Solution Approach 2:
Document processing actions are performed in advance during pre-processing stages. Text extraction, OCR, and feature generation are completed before the actual classification occurs, allowing the classification algorithm to operate quickly on pre-prepared features rather than processing everything in real-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
The system achieves reliable and efficient document classification across various formats, reduces computational burden, and ensures privacy by minimizing sensitive information transmission, enabling scalable and accurate document management.
Implementation Method 1
at least one of an interface to an optical character recognition engine and an optical character recognition engine
Data Source
AI summary
A system for managing documents, comprising: interfaces to a user interface, proving an application programming interface, a database of document images, a remote server, configured to communicate a text representation of the document from the optical character recognition engine to the report server, and to receive from the remote server a classification of the document; and logic configured to receive commands from the user interface, and to apply the classifications received from the remote server to the document images through the interface to the database. A corresponding method is also provided.