Using Tesseract OCR with, python - PyImageSearch

If you take a look at the project on GitHub youll see that the library is writing the image to a temporary file on disk followed by calling the tesseract  binary on the file and capturing the resulting output. Finally, we will compare the results of both of these methods and note any errors. Python Tesseract did a reasonable job here, but once again we have demonstrated the limitations of the library as an off-the-shelf classifier. This is where you would want to add more advanced pre-processing methods (depending on your specific application of OCR) which are beyond the scope of this blog post.

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I hope you are enjoying this series of blog posts on Optical Character Recognition (OCR) with Python and OpenCV! I'm here to share my tips, tricks, and hacks I've learned along the way. By, adrian Rosebrock on in, optical Character Recognition (OCR), Tutorials, in last weeks blog post we learned how to install the Tesseract binary for Optical Character Recognition (OCR).

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Not only will you get.zip of the code, Ill also send you a free 11-page Resource Guide on Computer Vision and Image Search Engines, including exclusive techniques that I dont post on this blog! Hence, we tend to train domain-specific image classifiers and detectors. Enter your email address below to get my free 11-page Image Search Engine Resource Guide PDF. Then we will run each image through   (which performs pre-processing before sending through Tesseract). The Image  class is required so that we can load our input image from disk in PIL format, a requirement when using pytesseract.

Handling CSV Files

Enough blather - here is the whole script. Lines 28-29 perform a median blur when the -preprocess  flag is set to blur. Hey, its your service after all.  #For Event Log break else: #Ok, here's the real money shot right here. This enabled us to apply OCR algorithms from within our Python script.

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The blobs act as distractors to our simple algorithm. This is followed by some cleanup on Line 39 where we delete the temporary file. In practice, it can be extremely challenging to guarantee these types of segmentations.

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Applying a median blur can help reduce salt and pepper noise, again making it easier for Tesseract to correctly OCR the image. If rownum 0: header row else: colnum 0 for col in row: print '-8s: s' (headercolnum, col) colnum 1 rownum 1 ose when run it produces: python A : 1 B : 2 C D : 3 4 A : 5 B :. Finally, lets try another image, this one with more text: Figure 5: Another example input to our Tesseract Python OCR system. To be notified when new blog posts are published here on PyImageSearch, be sure to enter your email address in the form below! Line 40 is where we print text to the terminal.

Decimal, Hexadecimal and Binary Conversion Chart

Lets begin by creating a new file named   : Lines 2-6 handle our imports. Next, well develop a simple Python script to load an image, binarize it, and pass it through the Tesseract OCR system.