Text Extraction System Using Multi-Approach OCR and Image Pre-Processing
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Solution Overview
Problem
Current methods for extracting textual information from image documents are laborious and inefficient, requiring manual data entry and processing, which delays document verification and decreases productivity in organizations relying on physical documents.
Innovation Solution
A Text Extraction System (TES) that performs character recognition using multiple approaches, applies image enhancements, and combines textual representations to determine the result text, utilizing image pre-processing techniques like rescaling, skew correction, and background cropping, along with optical character recognition (OCR) and regular expressions to extract and identify textual information from image documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data entry and processing is used to extract textual information from image documents, then accuracy can be maintained through human verification, but labor time and processing delays increase significantly
Solution Approach 1:
The system performs preliminary image enhancements (rescaling, skew correction, background cropping) and applies multiple character recognition approaches before final text combination, preparing the data in advance to enable faster automated processing while maintaining accuracy through pre-processed quality improvement
Solution Approach 2:
The system introduces an intermediary processing layer that combines textual representations from multiple OCR approaches and applies regular expressions as a mediator to identify and verify extracted texts, enabling automated processing to achieve accuracy comparable to manual verification without the time cost
2Measurement precision
If multiple character recognition approaches are used to extract text, then extraction accuracy improves through combined results, but system complexity increases
Solution Approach 1:
The system segments the text extraction task into multiple independent character recognition approaches (different OCR engines or methods), each processing the image separately, then combines their results. This segmentation allows parallel processing of multiple approaches without requiring complex interdependencies between them
Solution Approach 2:
The system merges the textual representations from multiple character recognition approaches into a single combined result, using regular expressions to identify and consolidate extracted texts. This combining process integrates multiple simple approaches into an accurate unified solution without requiring complex coordination
3Measurement precision
If image pre-processing techniques are applied to enhance image quality, then character recognition accuracy improves, but processing time increases
Solution Approach 1:
The system performs necessary image enhancements (rescaling to optimal dimensions, skew correction, background cropping) as preliminary actions before character recognition. By completing these enhancements in advance, the system ensures high recognition accuracy while the subsequent automated OCR processing compensates for the pre-processing time investment
Solution Approach 2:
The system changes key image parameters (resolution through rescaling, orientation through skew correction, composition through background cropping) to optimize the image for character recognition. These parameter changes improve recognition accuracy by transforming the image into an optimal state for subsequent automated processing
Data Source
AI summary
Aspects of the present disclosure are directed to extracting textual information from image documents. In one embodiment, a system, upon receiving a request to extract textual information from an image document, a digital processing system performs character recognition based on content of the image document using multiple approaches to generate corresponding texts. The texts are then combined to determine a result text representing the textual information contained in the image document. The result is then provided as a response to the request.