Understanding OCR and Improving Accuracy
This guide explains how OCR works in VNTranslator and provides practical tips to improve text recognition accuracy
Last updated
This guide explains how OCR works in VNTranslator and provides practical tips to improve text recognition accuracy
Last updated
The first step in the OCR process is capturing an image from the screen. The quality of the captured image significantly impacts the OCR engine to recognize text accurately.
This step is crucial for improving image quality. During pre-processing, the image is adjusted to display black text on a white background. This contrast makes it easier for the OCR engine to recognize the text.
Text recognition accuracy also depends on the chosen OCR engine. For example:
Traditional OCR Engines like Tesseract OCR and Windows OCR are optimized for recognizing black text on a white background. These engines work well in simple situations but may have trouble with colored text or complicated backgrounds. However, Windows OCR is slightly better than Tesseract OCR for recognizing colored text.
Modern OCR Engines like Google Cloud Vision or Azure Cloud Vision are more advanced and can recognize text with colored text or complex backgrounds.
After the OCR engine processes the text recognition, the result will be displayed. If recognition is inaccurate, corrections can be made during post-processing using Regular Expressions (RegExp) to refine the results.
Ensure High-Quality Image Captures: The better the quality of the screen capture, the higher the accuracy of OCR. Avoid blurry or low-resolution images.
Use Effective Pre-processing (For Tesseract OCR and WIndows OCR): Adjust the image to have high contrast (black text on a white background) to make text recognition easier for the OCR engine.
Select the Appropriate OCR Engine: Choose an OCR engine that suits the characteristics of your text and background. Use modern OCR engines for colored text and complex backgrounds.
Utilize Post-processing: If text recognition is incorrect or you want to remove and match specific characters, you can use RegExp during post-processing to refine the output.
