Understanding OCR and Improving Accuracy
This guide will briefly explain how the OCR works in VNTranslator and provide tips on how to improve its text recognition accuracy.
Last updated
This guide will briefly explain how the OCR works in VNTranslator and provide tips on how to improve its text recognition accuracy.
Last updated
The initial step in the OCR process involves capturing an image from the display screen. The quality of the captured image significantly impacts the OCR's ability to recognize text accurately.
This phase is crucial for enhancing the image quality. During pre-processing, the image is adjusted to have black text on a white background. This contrast is essential for the OCR engine to recognize text more easily.
The accuracy of text recognition also depends on the OCR engine used. For instance:
Traditional OCR Engines like Tesseract OCR or Windows OCR are designed to recognize black text on a white background. They work well under these conditions but may struggle with colored text or complex backgrounds.
Modern OCR Engines such as Google Cloud Vision or Azure Cloud Vision are more advanced and can recognize text in various colors and against complicated backgrounds.
Therefore, it's recommended to use Tesseract OCR or Windows OCR when the input image is clear, and the pre-processing (Image Processing) can convert the image into black text with a white background.
After the OCR engine processes the text recognition, the result will be displayed. If the recognition is not accurate, it can be corrected during the post-processing stage using features like Regular Expressions (RegExp) to fine-tune 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.
Utilize Effective Pre-processing: Adjust the image to have high contrast (black text on a white background) for traditional OCR engines. This step is crucial for enhancing the OCR’s ability to discern text.
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.
Leverage Post-processing Tools: Utilize the post-processing features like RegEx to correct any errors in recognition and refine the output.
