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    Individual Assignments of Emerging

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    M.Cedrick
    The document discusses using Python and OpenCV for license plate recognition (LPR). LPR uses image processing to identify vehicles by their license plates and has applications in security and traffic management. It proposes using Python and the open source OpenCV library as an alternative to proprietary tools like Matlab for building automatic license plate recognition systems.

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    Individual Assignments of Emerging

    Uploaded by

    M.Cedrick
    39 views4 pages
    The document discusses using Python and OpenCV for license plate recognition (LPR). LPR uses image processing to identify vehicles by their license plates and has applications in security and traffic management. It proposes using Python and the open source OpenCV library as an alternative to proprietary tools like Matlab for building automatic license plate recognition systems.

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    Original Title

    INDIVIDUAL ASSIGNMENTS OF EMERGING

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    The document discusses using Python and OpenCV for license plate recognition (LPR). LPR uses image processing to identify vehicles by their license plates and has applications in security and traffic management. It proposes using Python and the open source OpenCV library as an alternative to proprietary tools like Matlab for building automatic license plate recognition systems.

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    39 views4 pages

    Individual Assignments of Emerging

    Uploaded by

    M.Cedrick
    The document discusses using Python and OpenCV for license plate recognition (LPR). LPR uses image processing to identify vehicles by their license plates and has applications in security and traffic management. It proposes using Python and the open source OpenCV library as an alternative to proprietary tools like Matlab for building automatic license plate recognition systems.

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    of 4

    INDIVIDUAL ASSIGNMENTS OF EMERGING

    OUESTION 1

    Plate recognition with python

    License plate Recognition (LPR) is the capacity to capture photographic


    video or images from license plates and transform the optical data into
    digital information in real-time.

    License Plate Recognition using OpenCV Python License Plate Recognition is


    an image-processing technology used to identify vehicles by their
    license plates. This technology is used in various security and traffic
    applications.

    Automatic License Plate Recognition system is a real time embedded system which automatically
    recognizes the license plate of vehicles. There are many applications ranging from complex security
    systems to common areas and from parking admission to urban traffic control. Automatic license
    plate recognition (ALPR) has complex characteristics due to diverse effects such as of light and
    speed. Most of the ALPR systems are built using proprietary tools like Matlab. This paper presents
    an alternative method of implementing ALPR systems using Free Software including Python and the
    Open Computer Vision Library. Keywords—License plate, Computer Vision, Pattern Recognition,
    Python, OCR. 

    OpenCV
    OpenCV (Open Source Computer Vision) is a library of programming functions mainly aimed at real-
    time computer vision. Originally developed by Intel's research center in Nizhny Novgorod (Russia), it
    was later supported by Willow Garage and is now maintained by Itseez. The library is cross-platform
    and free for use under the open-source BSD license.

    Python is a widely used high-level, general-purpose, interpreted, dynamic programming language.


    Its design philosophy emphasizes code readability, and its syntax allows programmers to express
    concepts in fewer lines of code than possible in languages such as C++ or Java. The language
    provides constructs intended to enable writing clear programs on both a small and large scale

    License Plate Recognition is an image-processing technology used to


    identify vehicles by their license plates. This technology is used in
    various security and traffic applications. I made this using simple
    Python code.

    #@programming_fever

    import cv2

    import imutils

    import numpy as np

    import pytesseract

    pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files (x86)\Tesseract-OCR\tesseract.exe'

    img = cv2.imread('D://skoda1.jpg',cv2.IMREAD_COLOR)

    img = cv2.resize(img, (600,400) )

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    gray = cv2.bilateralFilter(gray, 13, 15, 15)


    edged = cv2.Canny(gray, 30, 200)

    contours = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    contours = imutils.grab_contours(contours)

    contours = sorted(contours, key = cv2.contourArea, reverse = True)[:10]

    screenCnt = None

    for c in contours:

    peri = cv2.arcLength(c, True)

    approx = cv2.approxPolyDP(c, 0.018 * peri, True)

    if len(approx) == 4:

    screenCnt = approx

    break

    if screenCnt is None:

    detected = 0

    print ("No contour detected")

    else:

    detected = 1

    if detected == 1:

    cv2.drawContours(img, [screenCnt], -1, (0, 0, 255), 3)

    mask = np.zeros(gray.shape,np.uint8)

    new_image = cv2.drawContours(mask,[screenCnt],0,255,-1,)

    new_image = cv2.bitwise_and(img,img,mask=mask)
    (x, y) = np.where(mask == 255)

    (topx, topy) = (np.min(x), np.min(y))

    (bottomx, bottomy) = (np.max(x), np.max(y))

    Cropped = gray[topx:bottomx+1, topy:bottomy+1]

    text = pytesseract.image_to_string(Cropped, config='--psm 11')

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