Dynamic Document Classification for Selective OCR Processing
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Solution Overview
Problem
Automated document processing is hindered by the inefficiency and resource intensity of optical character recognition (OCR) processes, particularly when dealing with large documents containing unnecessary textual content.
Innovation Solution
A system and method utilizing a dynamically trained classifier to differentiate between OCR regions, non-OCR valuable regions, and non-OCR non-valuable regions within documents, thereby optimizing OCR application and reducing unnecessary processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If OCR is applied to all textual regions in a document, then complete data extraction is achieved, but processing time and computational resources are wasted on non-valuable regions
Solution Approach 1:
The document is divided into multiple textual regions, and each region is classified independently as OCR-valuable or non-OCR valuable. This segmentation allows selective application of OCR only to relevant regions, avoiding unnecessary processing of entire documents while maintaining complete data extraction from valuable areas.
Solution Approach 2:
Different processing strategies are applied to different regions based on their characteristics. OCR is applied selectively to regions identified as valuable, while non-valuable regions are skipped. This local differentiation optimizes resource allocation and processing time while ensuring data completeness where needed.
2Loss of information
If OCR is applied to all textual regions, then all text is converted to machine-encoded text, but computational resources and processing power are unnecessarily consumed
Solution Approach 1:
The classifier extracts and identifies valuable textual regions from the document, separating them from non-valuable regions. OCR is then applied only to the extracted valuable regions, eliminating unnecessary computational resource consumption on irrelevant text while maintaining complete text conversion where needed.
Solution Approach 2:
Instead of applying OCR to all regions (excessive action), the system applies OCR only to the necessary valuable regions (partial action). The classifier determines the minimal set of regions requiring OCR processing, reducing computational resource consumption while ensuring all necessary text is converted.
3Device complexity
If a static classifier is used for document processing, then the system structure is simple, but the system cannot adapt to different document types and layouts
Solution Approach 1:
The classifier is designed to be dynamic and adaptive, capable of adjusting its behavior based on different document types and layouts. The system learns from training data and adapts to various document formats, maintaining reasonable structural complexity while achieving high versatility across different document kinds.
Solution Approach 2:
The classifier parameters and characteristics are adjusted based on the specific document being processed. By changing parameters according to document type and layout variations, the system achieves adaptability without requiring a completely different structure for each document kind, balancing complexity and versatility.
Data Source
AI summary
In an approach, a processor performs document layout analysis on a document generating a plurality of textual regions; extracts characteristics from each of the plurality of textual regions and associates the respective characteristics to the respective textual region as metadata; classifies each of the plurality of textual regions as an optical character recognition (OCR) region, non-OCR valuable region, or non-OCR non-valuable region using a classifier; performs OCR on each OCR region generating an OCR output; identifies associated constant OCR data from a constant OCR data repository for each non-OCR valuable region; merges the associated constant OCR data with the OCR output generating a complete OCR data for the received document; performs data extraction on the complete OCR data to identify data fields and key-value pairs generating extracted data; and determines whether the extracted data is valid based on a set of rules.