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====== The paperScanner python command ======

----
===== Introduction =====
PaperScanner is a python command to extract body text of printing character page picture (bad or not).\\
You can use this command with option ''python paperScanner.py --help'' to have more information.


----
===== Installation =====
Download the source code on github [[https://github.com/hiergaut/opencv/blob/master/paperScanner.py]]\\
\\
Of course you need to have opencv library,\\
some additional lib : PIL and pytesseracct to recognize character.\\
''pip install Pillow''\\
''pip install pytesseract''\\


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===== Usage =====
''python paperScanner.py --read <FILENAME>''\\
FILENAME is your picture file that you want to recover text character.\\


----
===== Explanation of source program =====

----
==== Crop and rotate target paper ====

Firstly we have a picture of text page like this\\
{{ :diy:projets:out.jpg?direct&400 |}}
\\
and we have to retrieve all sentence of this text.\\
\\
So before apply image treatment operations, I want to crop only the body text and align it.\\
I need to find the four corner of page before use warpPerspective function,\\
to eliminate other color unlike the white page, I use histogram to exclude other colors\\
{{ :diy:projets:screen.png?direct&200 |}}
On histogram, there are two peak, on left this is the yellow color chair,\\
and the other is the page color, seem as yellow more white that the precedent,\\
is not a perfect white page.\\
\\
I find the two boundary with this code
<code python>
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    hist = cv2.calcHist([img], [0], None, [256], [0, 256])

    # search max value on histogram
    M = hist[0]
    i = 0
    for j in range(1, 256):
        cur = hist[j]
        if cur > M:
            M = cur
            i = j

    if i > 253:
        prev = hist[i]
    else:
        prev = hist[i] + hist[i + 1] + hist[i + 2]

    # search first grow up on the right
    for j in range(i + 15, 254):
        cur = hist[j] + hist[j + 1] + hist[j + 2]
        if cur >= prev:
            break
        prev = cur

    right = j

    if i < 2:
        prev = hist[i]
    else:
        prev = hist[i - 2] + hist[i - 1] + hist[i]

    # search first grow up on the left
    for j in range(i - 15, 2, -1):
        cur = hist[j - 2] + hist[j - 1] + hist[j]
        if cur >= prev:
            break
        prev = cur

    left = j
</code>
after that, I make the contours, I see clearly the quadrilateral, and find the corners.
{{ :diy:projets:screen2.png?direct&400 |}}

<code python>
    match = cv2.approxPolyDP(cnt, 0.02 * len(cnt), True)
    
    [[p], [p2], [p3], [p4]] = match

    zoom = 1
    w = zoom * int(cv2.norm(p - p2))
    h = zoom * int(cv2.norm(p - p4))
    if w > h:
        t = w
        w = h
        h = t

        pts = np.float32([[p4], [p], [p2], [p3]])
    
    else:
        pts = np.float32([[p], [p2], [p3], [p4]])

    pts2 = np.float32([[w, 0], [0, 0], [0, h], [w, h]])
    M = cv2.getPerspectiveTransform(pts, pts2)
    img2 = cv2.warpPerspective(img_src, M, (w, h))
</code>

the result :\\
{{ :diy:projets:screen3.png?direct&400 |}}


----
==== Treatment (Thresholding, blurring, etc) ====

So now we must treat text character before launch tesseract recognition
I remove the margin to remove folding
<code python>
    h, w = img.shape[:2]
    
    margin = 100
    img = img[margin:h - margin, margin: w - margin]
</code>

Treatment to improve the quality and the sharpness of character
<code python>
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
</code>


----
==== Character Recognition ====

finally I use tesseract and check if each word exist in a language text dictionary
<code python>
    img2 = Image.fromarray(img)
    txt = pytesseract.image_to_string(img2, lang='fra')

    file =open('frenchWord.txt', 'r')
    keyword_list = file.read().split()

    cpt =0
    for word in txt.split():
        if word in keyword_list:
            print(word)
            cpt +=1
    
    nbWord =len(txt.split())

    print("\naccuracy = ", cpt, '/', nbWord, ' ', "%.1f" % (cpt *100 /nbWord), "%")
</code>
after ten seconds, I find 34.4% correct French word in the text.
{{:diy:projets:screen4.png?direct&400|}}