Image Quality Evaluation for OCR Accuracy
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Solution Overview
Problem
Conventional image processing methods for Optical Character Recognition (OCR) do not effectively evaluate image quality based on various image parameters, leading to inaccurate conversions, particularly for images with distortions such as low brightness, skewness, and warping, which can result in financial errors in documents like bills and receipts.
Innovation Solution
An image evaluation system determines image parameter values for parameters like brightness, skewness, and warping by segmenting images into binary segments, calculates suitability and impact values, and assigns a quality score to decide whether the image is suitable for OCR processing, correcting images as needed to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image processing methods are used for OCR, then the process is simple and fast, but the OCR accuracy is low due to poor image quality evaluation
Solution Approach 1:
The image is divided into multiple segments, and quality parameters are evaluated for each segment individually. This allows localized quality assessment and correction, improving overall OCR accuracy while managing complexity through modular processing of image regions.
Solution Approach 2:
The system performs preliminary quality evaluation and correction of image parameters (brightness, contrast, sharpness, color) before OCR processing. By pre-processing and correcting image quality issues in advance, the system ensures high-quality input for OCR, improving accuracy without adding complexity during the main recognition process.
2Reliability
If all images are processed for OCR without quality check, then processing speed is high, but conversion accuracy is low leading to financial errors
Solution Approach 1:
The system performs preliminary quality evaluation before OCR conversion to identify images that meet quality thresholds. This preliminary filtering ensures that only sufficiently quality images proceed to OCR processing, maintaining high conversion accuracy while avoiding unnecessary processing of poor-quality images.
Solution Approach 2:
The system evaluates image quality parameters and provides feedback on whether images meet the required quality threshold for OCR processing. This feedback mechanism allows the system to make informed decisions about which images to process, ensuring accuracy while managing processing throughput efficiently.
Data Source
AI summary
The present subject matter is related in general to the field of image processing, disclosing method and system for evaluating an image quality for Optical Character Recognition (OCR) Image evaluation system receives image comprising optical character data. The image evaluation system determines image parameter value for each of one or more image parameters of the image. The image parameter value for each of the one or more image parameters is determined for plurality of binary image segments identified in the image. The image evaluation system determines suitability value and impact value of the image, based on the image parameter value for each of the image parameters determined for the image. The image evaluation system determines quality score for the image, based on the suitability value and the impact value. The image is transmitted for processing before the OCR, upon determining the quality score to be above overall pre-defined threshold value.