A unique non-parametric unattended approach to correct unwanted document image distortions to achieve optimal OCR accuracy. It applies a highly effective stack of document image enhancement algorithms to restore perfect images distorted by unknown sources of distortion. First, it provides a means of modifying local brightness and contrast in order to better handle different illumination levels and atypical light transmission patterns in the image. Then apply a nifty grayscale conversion method to your photo to give it a new look. Third, it uses unsharp masking techniques to further enhance important details in grayscale images. Finally, we use the best global binarization technique to prepare the final document image for OCR recognition. The proposed technique has the potential to significantly improve the text recognition rate and accuracy of optical character recognition.
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Contrast enhancement technique to improve OCR engine (Pytesseract used here). Histogram equalization and Otsu binarization are used.
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nithin-k-shine/Contrast-Enhancement-for-OCR
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Contrast enhancement technique to improve OCR engine (Pytesseract used here). Histogram equalization and Otsu binarization are used.
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