Plate Number Detection & Recognition

1. Install and Import Dependencies
1. Install OpenCV
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pip3 install opencv-python

2. Install EasyOCR
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pip3 install easyocr
pip3 install imutils
# For the latest development release:
pip install git+https://github.com/JaidedAI/EasyOCR.git

# -------- [Simple example ] ----------
import easyocr
# you can change the language to match your prefered one. [ 'ar' for Arabic , en English and many others]
reader = easyocr.Reader(['ch_sim','en']) # this needs to run only once to load the model into memory
result = reader.readtext('chinese.jpg') # the file you want to read the text from

3. Import these libraries to your code
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import cv 
from matplotlib import pyplot as plt
import numpy as np 
import imutils
import easyocr

2. Read in Image, Grayscale and Blur
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img = cv2.imread('image4.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plt.imshow(cv2.cvtColor(gray, cv2.COLOR_BGR2RGB))

3. Apply filter and find edges for localization
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bfilter = cv2.bilateralFilter(gray, 11, 17, 17) #Noise reduction
edged = cv2.Canny(bfilter, 30, 200) #Edge detection
plt.imshow(cv2.cvtColor(edged, cv2.COLOR_BGR2RGB))

4. Find Contours and Apply Mask
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keypoints = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = imutils.grab_contours(keypoints)
contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]location = None

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location = None
for contour in contours:
    approx = cv2.approxPolyDP(contour, 10, True)
    if len(approx) == 4:
        location = approx
        break

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location

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mask = np. zeros(gray.shape, np.uint8)
new_image = cv2. drawContours (mask, [location], 0,255, -1)
new_image = cv2.bitwise_and(imgs, imgs, mask=mask)

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plt. imshow(cv2. cvtColor (new_image, cv2. COLOR_BGR2RGB) )

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(x,y) = np.where(mask==255)
(x1, y1) = (np.min(x), np.min(y))
(x2, y2) = (np.max(x), np.max(y))
cropped_image = gray[x1:x2+1, y1:y2+1]plt.imshow(cv2.cvtColor(cropped_image, cv2.COLOR_BGR2RGB))

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plt.imshow(cv2. cvtColor(cropped_image, cv2.COLOR_BGR2RGB))

5. Use Easy OCR To Read Text
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reader = easyocr.Reader(['en'])
result = reader.readtext(cropped_image)
result

6. Render Result
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text = result[0][-2]
font = cv2.FONT_HERSHEY_SIMPLEX
res = cv2.putText(img, text=text, org=(approx[0][0][0], approx[1][0][1]+60), fontFace=font, fontScale=1, color=(0,255,0), thickness=2, lineType=cv2.LINE_AA)
res = cv2.rectangle(img, tuple(approx[0][0]), tuple(approx[2][0]), (0,255,0),3)
plt.imshow(cv2.cvtColor(res, cv2.COLOR_BGR2RGB))

Input images:

Didn’t work correctly with these images! Need better fining for images