Now run it through the Tesseract binary without any preprocessing, using the prevous code to execute Tesseract in the shell:. We now preprocess the image to make the text stand out as much as possible from the background. This is done using a combination of thresholding, as dealt with earlier, and morphological adjustments.
We'll again use OpenCV for this. As before, the image is first converted to grayscale. A Gaussian blur is then applied to further take out noise. The other operations concern the text itself, thresholding and dilating it to separate the text from the background. The final step inverts the image color wise, from black to white and vice versa so the text is black in the end and displayed on a white background.
See for the excellent solution on StackOverflow here. It looks as follows:. As can be seen from the output, Tesseract now correctly extracts the text from the image even though the text itself is still blurry and some of the pixels in the letters are disconnected.
Hence upon pre-processing the image, the pre-trained models in tesseract, that have been trained on millions of characters, perform pretty well. Hence machine learning is very useful for OCR purposes. This tutorial is a first step in optical character recognition OCR in Python. It uses the excellent Tesseract package to extract text from a scanned image. This technique is relevant for many cases. For instance, historical documents that have not been digitalized yet, or have been digitalized incorrectly, come to mind.
Often you can make most progress by spending time on preprocessing an image carefully and taking out as much as noise as possible. The same noise that prevents Tesseract from being able to extract text also often prevents commercial alternatives from extracting text correctly. Removing noise from images for OCR purposes usually involves a lot of trial and error. One way to deal with this problem is to train Tesseract yourself so that it gets more familiar with the type of images and type of text you're working with.
However, it seems Capture needs further work to fix the bugs regarding translation and OCR. You just need to drag and drop the file, then choose output format and file language to start the OCR progress. You may have crash issues on latest operating systems. If your file is reversed, you can rotate to have a more accurate OCR. Also, it supports spell check, you can replace those suspected errors with words from the dictionary. Yet, it may take you some time to manually adjust the errors. Though it is designed to convert files to editable Word, the formatting cannot keep in the Word file.
In addition, the last update for this program was released in , there may be some technical errors on different, especially latest Windows. What's new in this version Version 4. Additional information Published by Define Studio. Published by Define Studio. Copyright Define Studio.
Developed by Define Studio. Approximate size Age rating For all ages. This app can Use your webcam Access your Internet connection Use your video library Use data stored on an external storage device Use your music library Use your pictures library.
Permissions info. Installation Get this app while signed in to your Microsoft account and install on up to ten Windows 10 devices. Language supported English United States. Updated Aug 25, Python. Updated Oct 3, Python. Updated Feb 20, Python. Sponsor Star Updated Nov 18, JavaScript. Updated Sep 24, Swift. Updated Jun 9, Python. Updated Aug 26, C. Seven Segment Optical Character Recognition. Updated Nov 10, C. A scientific document recognition system. Updated Oct 26, Java.
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