Document Class-Based OCR Information Extraction
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Solution Overview
Problem
Current optical character recognition (OCR) technologies face challenges in accurately capturing specific information from documents of varying classes, as they lack the ability to adaptively identify and extract information based on document class and field information, leading to inefficiencies in information retrieval and processing.
Innovation Solution
A system that includes processors to analyze documents, identify their class, determine field information associated with the class, and perform OCR to capture specific information, allowing users to review and update field information for improved performance across similar document types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional OCR technology is used to capture information from documents, then the basic text recognition function is provided, but the ability to accurately capture specific information based on document class and field information is lacking
Solution Approach 1:
The system dynamically adapts its information extraction approach based on the identified document class. Field information structures are selected and applied dynamically according to the document type, allowing the OCR process to adjust its parameters and focus areas dynamically, thereby improving both accuracy and adaptability simultaneously
Solution Approach 2:
The system changes operational parameters by selecting different field information structures corresponding to different document classes. By identifying the document class first, the system adjusts extraction parameters such as field locations, data formats, and validation rules to match the specific document type, resolving the contradiction between precision and adaptability
2Measurement precision
If document analysis is performed to identify document class and field information, then the accuracy of information extraction is improved, but the processing time and system complexity increase
Solution Approach 1:
The system performs preliminary document class identification and field information structure selection before the actual OCR extraction process. By pre-configuring the appropriate field structures based on document class, the system avoids time-consuming trial-and-error extraction attempts, thereby improving accuracy without excessive time penalty
Solution Approach 2:
The processing is segmented into distinct phases: document class identification, field information structure selection, and specific information extraction. This segmentation allows each phase to be optimized independently, with the class identification phase being fast and the extraction phase being precise, thus balancing time and accuracy
3Productivity
If field information is determined based on document class, then the efficiency of capturing specific information is improved, but the device complexity increases
Solution Approach 1:
The system employs a universal framework where a single document class identification module serves multiple document types, and a library of field information structures provides multi-functional support for various extraction needs. This universal approach improves efficiency while controlling complexity through reuse and standardization
Solution Approach 2:
Field information structures serve as intermediary data structures that bridge the gap between document class identification and specific information extraction. These intermediaries encapsulate the complexity of different document formats, allowing the core extraction engine to remain simple while still handling diverse document types efficiently
Data Source
AI summary
A device may obtain a document, of a document type, from which specific information is to be captured. The specific information to be captured may depend on a document class associated with the document. The device may identify the document class, associated with the document, based on a characteristic of the document. The document class may identify a category of document in which the document is included, and may be associated with multiple document types. The device may determine field information associated with the document class. The field information may include information that identifies a portion of the document where the specific information is located, or may include information that identifies a manner in which the specific information can be identified within the document. The device may analyze the document, based on the field information, in order to capture the specific information. The device may provide the captured specific information.