OCR on Semi-Structured Documents Using Spatial Classifier Segmentation
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Solution Overview
Problem
Traditional OCR techniques fail to efficiently extract data from semi-structured documents like tax forms due to variations in layout and design, leading to sub-par results and the need for manual data entry, which is time-consuming and error-prone, especially on resource-constrained devices like mobile phones.
Innovation Solution
A classifier model is trained using spatial attributes to identify label regions in documents without performing OCR, allowing for the accurate distinction between labels and values, and subsequent OCR is performed only on value regions to extract corresponding values, reducing the need for manual data entry and improving efficiency on resource-constrained devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional OCR techniques are used on semi-structured documents with varying layouts, then the system can process documents without manual intervention, but the extraction accuracy deteriorates due to layout variations
Solution Approach 1:
The patent segments the document processing task into distinct phases: first identifying label regions using spatial attributes and trained classifier models, then performing OCR only on value regions. This segmentation allows the system to handle layout variations by treating labels and values differently, improving extraction accuracy while maintaining automation.
Solution Approach 2:
The patent performs preliminary classification of document regions into labels and values before executing OCR. By using spatial attributes and trained classifier models to identify label regions first, the system prepares the document structure in advance, enabling more accurate subsequent OCR on value regions only.
2Loss of information
If OCR is performed on entire documents to ensure all data is captured, then completeness of data extraction is improved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts and identifies label regions from documents using spatial attributes and classifier models, then performs OCR only on the remaining value regions. This extraction approach ensures all necessary data is captured while significantly reducing the area subjected to computationally intensive OCR processing, thereby decreasing processing time.
Solution Approach 2:
Instead of applying OCR to the entire document (excessive action), the patent applies OCR only to value regions after identifying and excluding label regions (partial action). This partial application of OCR maintains data completeness while reducing computational overhead and processing time.
3Loss of information
If OCR is performed on entire documents to capture all data, then data extraction completeness is improved, but computational resource consumption increases
Solution Approach 1:
The patent extracts label regions from documents using spatial attributes and trained classifier models, then performs OCR only on value regions. This extraction strategy ensures complete data capture while minimizing the computational resources required for OCR processing by limiting it to necessary regions only.
Solution Approach 2:
The patent applies OCR partially only to value regions rather than excessively to the entire document. This partial application maintains information completeness while significantly reducing computational resource consumption, making the system more efficient on resource-constrained devices.
4Measurement precision
If manual data entry is used to handle layout variations, then data extraction accuracy is improved, but labor time and error rate increase
Solution Approach 1:
The patent enables the system to automatically handle layout variations through self-service mechanisms: trained classifier models automatically identify label regions using spatial attributes, and OCR automatically extracts values from identified regions. This automation eliminates the need for manual intervention while maintaining high extraction accuracy, thereby reducing both time and error rate.
Data Source
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AI summary
Techniques are disclosed for facilitating optical character recognition (OCR) by identifying one or more regions in an electronic document to perform the OCR. For example a method for identifying information in an electronic document includes obtaining a set of training documents for each template of a plurality of templates for the electronic document, extracting spatial attributes for at least a first label region and at least a first corresponding value region from the set, and training a classifier model based on the extracted spatial attributes, wherein the classifier model is used to identify the information in the electronic document. The spatial attributes represent a position of at least the first label region and at least the first value region within the electronic document.