Ndtyjky

I'm new to pyhton and I'm making a number plate recognition system using haar cascade. My code works fine to detect the number plate and make contours but pytesseract ocr fails to recognise the characters and gives strange results. Please help.

The detected plate using haar cascades

contours made on the detected region

This is the output

import cv2

import numpy as np

import pytesseract



plate_cascade = cv2.CascadeClassifier('C:/Users/Jai/Desktop/Test/haarcascade_plate.xml')



img = cv2.imread('C:/Users/Jai/Desktop/Test/images/NumberPlates/IMG_20181029_194221.jpg', cv2.IMREAD_COLOR)



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

gray = np.array(gray, dtype='uint8')

cv2.imshow('gray', gray)



plates = plate_cascade.detectMultiScale(gray, 1.3, 5)



for (x,y,w,h) in plates:

    cv2.rectangle(img, (x,y), (x+w,y+h),(255,0,0),5)

    roiGray = gray[y:y+h, x:x+w]

    roiImg = img[y:y+h, x:x+w]



roiUGray = cv2.bitwise_not(roiGray)

cv2.imshow('img', img)

cv2.imshow('roi_gray', roiUGray)



ret, thresh = cv2.threshold(roiUGray, 127, 255,0)

cv2.imshow("img", img)

height, width = thresh.shape

newImage = np.zeros((height, width, 3), np.uint8)

newImage[:, :] = (0,0,0)

im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)



num = 0

area = 0



for j in range(len(contours)):

    if hierarchy[0][j][3] != -1:

        area = area + cv2.contourArea(contours[j])

        num += 1



if num != 0:

    avgArea = float(area)/float(num)



for j in range(len(contours)):

    if hierarchy[0][j][3] != -1 and cv2.contourArea(contours[j]) > 0.2*avgArea:

        cv2.drawContours(newImage, contours[j], -1, (255,255,255), 1)



blur = cv2.GaussianBlur(newImage, (1, 1), 0)

cv2.imshow("roi", blur)



ocr_result = pytesseract.image_to_string(blur, lang='eng')

print(ocr_result)



cv2.waitKey(0)

cv2.destroyAllWindows()

This method uses contour detection to find the area of the number plate, and does a perspective transform on it. It then uses adaptive thresholding to detect the digits and does a medianBlur to get rid of noise that messes up the pytesseract operation.

The blue box is from the original image. The red box is from my number plate recognition haar cascade. The green is the contour detection of the number plate.

Here's the output of the perspective transform. I used the imutils module to do this.

This is the output of the adaptive thresholding and blurring, which I used the skimage module for.

Using this I got the output of:

fUP1ADN7120

And removing all characters that aren't uppercase or a digit gives:

UP1ADN7120

Which is only one character wrong.

This method isn't too bad, but you could get even better using another method. If this didn't work for you, then you could create a CNN.

Here's the code:

import cv2

import numpy as np

import pytesseract

import imutils.perspective

from skimage.filters import threshold_local



plate_cascade = cv2.CascadeClassifier('/usr/local/lib/python3.5/dist-packages/cv2/data/haarcascade_russian_plate_number.xml')



img = cv2.imread('GNA0d.jpg', cv2.IMREAD_COLOR)



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

gray = np.array(gray, dtype='uint8')



plates = plate_cascade.detectMultiScale(gray, 1.3, 5)



for (x,y,w,h) in plates:

    cv2.rectangle(img, (x,y), (x+w,y+h),(0,0,255),2)

    roiGray = gray[y:y+h, x:x+w]

    roiImg = img[y:y+h, x:x+w]



blur = cv2.GaussianBlur(roiGray, (5, 5), 0)

edges = cv2.Canny(blur, 75, 200)



contours = cv2.findContours(edges.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[1]

contours = sorted(contours, key = cv2.contourArea, reverse = True)[:5]  #sort the contours, only getting the biggest to improve speed



for contour in contours:

    peri = cv2.arcLength(contour, True)

    approx = cv2.approxPolyDP(contour, 0.02 * peri, True)



    #find contours with 4 edges

    if len(approx) == 4:

        screenCnt = approx

        break



orig = roiImg.copy()

cv2.drawContours(roiImg, [screenCnt], -1, (0, 255, 0), 2)



#do a perspective transform

warped = imutils.perspective.four_point_transform(orig, screenCnt.reshape(4, 2))

graywarp = imutils.perspective.four_point_transform(roiGray, screenCnt.reshape(4, 2))



#threshold using adaptive thresholding

T = threshold_local(graywarp, 11, offset = 10, method = "gaussian")

graywarp = (graywarp > T).astype("uint8") * 255



#do a median blur to remove noise

graywarp = cv2.medianBlur(graywarp, 3)



text = pytesseract.image_to_string(graywarp)

print(text)

print("".join([c for c in text if c.isupper() or c.isdigit()]))





cv2.imshow("warped", warped)

cv2.imshow("graywarp", graywarp)

cv2.imshow('img', img)





cv2.waitKey(0)

cv2.destroyAllWindows()

Quite a bit was taken from pyimagesearch, who explains it better than I ever could.