Grayscale Image Preprocessing for OCR Accuracy
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Solution Overview
Problem
Existing methods for processing screen images to extract alphanumeric characters are hindered by low resolution, making it difficult for optical character recognition (OCR) software to accurately recognize textual information, as the resolution of computer screen images is typically insufficient for effective character recognition.
Innovation Solution
A computer-implemented method that transforms grayscale images into OCR images by increasing resolution, applying filters to remove noise, and sharpening the images through anti-aliasing, thereby enhancing the quality and resolution to a range suitable for OCR engines to recognize alphanumeric characters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the resolution of screen images is increased to improve character recognition accuracy, then the quality of OCR input improves, but the processing complexity and computational resources required increase
Solution Approach 1:
The patent applies preliminary actions by performing multiple image processing operations (filtering, anti-aliasing, sharpening) before the OCR recognition step. This prepares the low-resolution screen image in advance to meet the quality requirements for accurate character recognition, resolving the contradiction by pre-enhancing image quality rather than requiring high-resolution capture.
Solution Approach 2:
The patent changes multiple image parameters including resolution, noise levels, sharpness, and anti-aliasing properties through a series of processing steps. By systematically adjusting these parameters, the image quality is transformed from low-resolution screen output to OCR-suitable quality, achieving high recognition accuracy without requiring high-resolution capture.
2Measurement precision
If multiple image processing steps are applied to enhance image quality, then OCR recognition accuracy improves, but the processing time increases
Solution Approach 1:
The patent implements continuous useful action by applying multiple processing steps (filtering, anti-aliasing, sharpening) in a continuous pipeline rather than discrete separate operations. This allows the image to be progressively enhanced through each step, maintaining momentum and efficiency while achieving the cumulative effect needed for high OCR accuracy.
Solution Approach 2:
The patent segments the image processing task into distinct, manageable steps (noise filtering, anti-aliasing, sharpening, resolution adjustment). Each segment addresses a specific quality issue independently, allowing for optimized processing of each function and facilitating parallel or sequential execution based on computational resources available.
3Measurement precision
If noise filtering is applied to improve image quality, then character recognition accuracy improves, but details and fine structures may be removed
Solution Approach 1:
The patent applies local quality by using selective filtering approaches that treat different regions of the image differently. Noise filtering is applied preferentially to background and uniform areas, while character regions receive different processing intensity. This preserves fine details within characters while removing noise from surrounding areas, maintaining recognition accuracy without losing essential image information.
Data Source
AI summary
A computer implemented that, in various aspects, transforms a grayscale image into an OCR image by applying a combination of steps to the grayscale image is disclosed herein. The grayscale image may include one or more alphanumeric characters. The grayscale image may includes visual content displayed upon a computer screen, so that the grayscale image may have a resolution generally equivalent to a screen resolution of the computer screen prior to application of the combination of steps thereto. The resultant OCR image, in various implementations, is of sufficient resolution and quality that an OCR engine can generally recognize alphanumeric characters imbedded within the OCR image.