CODEWITHSUNDEEP
pythonopencvimage-processingocrobject-detection
Michael Nguyen
Michael Nguyen

Reputation: 93

Check if the rectangle contour contains numbers inside or not? (OpenCV - Python)

I know the quality is so so bad, but that's original image

Original Image

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
rectKern = cv2.getStructuringElement(cv2.MORPH_RECT, (85, 64))
blackhat = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, rectKern)

Blackhat

edges = cv2.Canny(light, 120, 255, 1)

Edge detection using Canny

squareKern = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
light = cv2.morphologyEx(gray, cv2.MORPH_OPEN, squareKern)
light = cv2.threshold(light, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

Morphological Transformation using Opening method

Here is the result

Result

How can I check if inside that blue rectangle, there are numbers or not? (if there is no number inside, then I won't draw a bounding box around it since it's not license plate).

Upvotes: 2

Views: 1011

Answers (1)

Abhi25t
Abhi25t

Reputation: 4643

There are multiple methods for this. Choose depending on your requirement.

1- OCR via pytesseract - crop the rectangular region and then pass it to the tesseract to extract the text from the image.

# Import required packages 
import cv2 
import pytesseract 
  
# Mention the installed location of Tesseract-OCR in your system 
pytesseract.pytesseract.tesseract_cmd = 'System_path_to_tesseract.exe'
  
# Read image from which text needs to be extracted 
img = cv2.imread("sample.jpg") 
  
# Preprocessing the image starts 
  
# Convert the image to gray scale 
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
  
# Performing OTSU threshold 
ret, thresh1 = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV) 
  
# Specify structure shape and kernel size.  
# Kernel size increases or decreases the area  
# of the rectangle to be detected. 
# A smaller value like (10, 10) will detect  
# each word instead of a sentence. 
rect_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (18, 18)) 
  
# Appplying dilation on the threshold image 
dilation = cv2.dilate(thresh1, rect_kernel, iterations = 1) 
  
# Finding contours 
contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL,  
                                                 cv2.CHAIN_APPROX_NONE) 
  
# Creating a copy of image 
im2 = img.copy() 
  
# A text file is created and flushed 
file = open("recognized.txt", "w+") 
file.write("") 
file.close() 
  
# Looping through the identified contours 
# Then rectangular part is cropped and passed on 
# to pytesseract for extracting text from it 
# Extracted text is then written into the text file 
for cnt in contours: 
    x, y, w, h = cv2.boundingRect(cnt) 
      
    # Drawing a rectangle on copied image 
    rect = cv2.rectangle(im2, (x, y), (x + w, y + h), (0, 255, 0), 2) 
      
    # Cropping the text block for giving input to OCR 
    cropped = im2[y:y + h, x:x + w] 
      
    # Open the file in append mode 
    file = open("recognized.txt", "a") 
      
    # Apply OCR on the cropped image 
    text = pytesseract.image_to_string(cropped) 
      
    # Appending the text into file 
    file.write(text) 
    file.write("\n") 
      
    # Close the file 
    file.close

Source: Link

2- opencv's EAST text detector - Tutorial

Also, have a look at this question for more methods

And this as well

Upvotes: 4

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