OCR Text Verification via Pixel Transformation
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Solution Overview
Problem
Conventional optical character recognition (OCR) systems are prone to errors when processing low-quality images, leading to inaccuracies in detecting text from scanned documents, which requires manual verification by humans, introducing fatigue and error-prone processes.
Innovation Solution
A method and system that utilize a native digital document with both an image and text layer to automatically verify OCR-detected text by determining pixel-based coordinates and applying pixel transformations to ensure accurate text detection and output, reducing reliance on human review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If OCR is performed on scanned documents to detect text, then text extraction efficiency is improved, but detection accuracy deteriorates due to errors in low-quality images
Solution Approach 1:
The patent introduces an intermediary verification layer that compares OCR-detected text against the original image data. The system extracts text coordinates from OCR results, retrieves corresponding image regions, and performs visual verification to confirm text accuracy. This intermediary step acts as a mediator between the OCR engine and final output, resolving the contradiction by maintaining both efficiency (through automated OCR) and accuracy (through image-based verification).
Solution Approach 2:
The system implements a feedback mechanism where OCR results are continuously verified against the source image. When discrepancies are detected between OCR text and actual image content, the system flags these for correction. This feedback loop enables the system to self-correct errors while maintaining high processing speed, thus resolving the trade-off between efficiency and accuracy.
2Measurement precision
If manual verification is performed to correct OCR errors, then text detection accuracy is improved, but processing time increases and human fatigue introduces errors
Solution Approach 1:
The patent enables the system to perform self-verification of OCR results by automatically comparing detected text against the original image data. The system extracts text regions based on OCR coordinates, retrieves corresponding image pixels, and verifies text accuracy without human intervention. This self-service approach eliminates human fatigue while maintaining high accuracy and reducing processing time compared to manual verification.
Solution Approach 2:
The system replaces the mechanical process of manual human verification with an automated computational process. Instead of human operators visually checking OCR results, the system uses coordinate-based image region extraction and automated text verification algorithms. This substitution eliminates human fatigue and significantly reduces the time required for verification while maintaining or improving accuracy.
3Adaptability or versatility
If OCR is performed on poor quality document images, then text extraction capability is improved, but error rate increases leading to unreliable results
Solution Approach 1:
The patent performs preliminary verification of OCR results by extracting text regions from the original image data before final output generation. The system uses OCR-detected coordinates to pre-extract and verify text regions, identifying potential errors early in the process. This preliminary action allows the system to handle poor quality images effectively by catching errors before they propagate to the final output, thus maintaining both adaptability and reliability.
Solution Approach 2:
The system applies preliminary anti-action by proactively identifying and correcting potential OCR errors before they affect the final output. By comparing OCR results against the original image data at an early stage, the system prevents error propagation. This preliminary counter-measure enables reliable text extraction from poor quality images by neutralizing the harmful effects of OCR errors before they can compromise output reliability.
Data Source
AI summary
Methods for automatically verifying text detected by optical character recognition (OCR). The method includes obtaining a native digital document having an image layer comprising a matrix of computer-renderable pixels and a text layer comprising computer-readable encodings of a sequence of characters. The method includes obtaining OCR-detected text from the image layer of the native digital document and a pixel-based coordinate location of the OCR-detected text in the image layer of the native digital document. The method includes determining, using a pixel transformation, a computer-interpretable location of the OCR-detected text in the text layer of the native digital document. The method includes detecting text in the text layer based on the computer-interpretable location of the OCR-detected text in the text layer. The method includes rendering only the detected text in the text layer when the OCR-detected text does not match the detected text in the text layer.