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<meta charset="utf-8">
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<title>Tasks of the Contingent Librarian</title>
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<link rel="stylesheet" type="text/css" href="tasks.css">
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<script src="tasks.js"></script>
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<body>
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<div class="cardback"><DOCUMENT_FRAGMENT><div class="mw-parser-output"><div class="thumb tright"><div class="thumbinner" style="width:152px;"><a class="image" href="File:Rereferencing_Open_work_OCR.jpeg.html"><img alt="" class="thumbimage" decoding="async" height="106" src="/mw-mediadesign/images/thumb/3/3c/Rereferencing_Open_work_OCR.jpeg/150px-Rereferencing_Open_work_OCR.jpeg" srcset="/mw-mediadesign/images/thumb/3/3c/Rereferencing_Open_work_OCR.jpeg/225px-Rereferencing_Open_work_OCR.jpeg 1.5x, /mw-mediadesign/images/thumb/3/3c/Rereferencing_Open_work_OCR.jpeg/300px-Rereferencing_Open_work_OCR.jpeg 2x" width="150"></a> <div class="thumbcaption"><div class="magnify"><a class="internal" href="File:Rereferencing_Open_work_OCR.jpeg.html" title="Enlarge"></a></div>A bootleg copy of The Open Work by Umberto Eco. OCR software has mistaken the page number (page 80) as the word “So”</div></div></div>
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<p>Snippets:
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</p>
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<h2><span class="mw-headline" id="Pre-processing_for_OCR">Pre-processing for OCR</span><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/mw-mediadesign/index.php?title=User:Simon/self_directed_research/OCR_preprocessing&action=edit&section=T-1" title="Edit section: ">edit</a><span class="mw-editsection-bracket">]</span></span></h2>
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<p>This script applies transformations to the image before running OCR, resulting in a clearer result:
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</p><p><br>
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</p>
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<pre># import the necessary packages
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#from PIL
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import Image
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import pytesseract
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import argparse
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import cv2
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import os
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# construct the argument parse and parse the arguments
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ap = argparse.ArgumentParser()
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ap.add_argument("-i", "--image", required=True,
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help="path to input image to be OCR'd")
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ap.add_argument("-p", "--preprocess", type=str, default="thresh",
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help="type of preprocessing to be done")
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args = vars(ap.parse_args())
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# load the example image and convert it to grayscale
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image = cv2.imread(args["image"])
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# check to see if we should apply thresholding to preprocess the
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# image
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if args["preprocess"] == "thresh":
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gray = cv2.threshold(gray, 0, 255,
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cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
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# make a check to see if median blurring should be done to remove
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# noise
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elif args["preprocess"] == "blur":
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gray = cv2.medianBlur(gray, 3)
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# write the grayscale image to disk as a temporary file so we can
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# apply OCR to it
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filename = "{}.png".format(os.getpid())
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cv2.imwrite(filename, gray)
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# load the image as a PIL/Pillow image, apply OCR, and then delete
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# the temporary file
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text = pytesseract.image_to_string(Image.open(filename))
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os.remove(filename)
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print(text)
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# show the output images
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cv2.imshow("Image", image)
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cv2.imshow("Output", gray)
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cv2.waitKey(0)
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</pre>
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