How Do I Detect Vertical Text With Opencv For Extraction

I am new to OpenCV and trying to see if I can find a way to detect vertical text for the image attached. In this case on row 3 , I would like to get the bounding box around Origina

Solution 1:

Here is my solution based on Kanan Vyas and Adrian Rosenbrock

It's probably not as "canonical" as you'd wish. But it seems to work (more or less...) with the image you provided.

Just a word of CAUTION: The code looks within the directory from which it is running, for a folder named "Cropped" where cropped images will be stored. So, don't run it in a directory which already contains a folder named "Cropped" because it deletes everything in this folder at each run. Understood? If you're unsure run it in a separate folder.

The code:

# Import required packages import cv2 
import numpy as np
import pathlib

#################################################################################################################################### https://www.pyimagesearch.com/2015/04/20/sorting-contours-using-python-and-opencv/###################################################################################################################################defsort_contours(cnts, method="left-to-right"):
    # initialize the reverse flag and sort index
    reverse = False
    i = 0# handle if we need to sort in reverseif method == "right-to-left"or method == "bottom-to-top":
        reverse = True# handle if we are sorting against the y-coordinate rather than# the x-coordinate of the bounding boxif method == "top-to-bottom"or method == "bottom-to-top":
        i = 1# construct the list of bounding boxes and sort them from top to# bottom
    boundingBoxes = [cv2.boundingRect(c) for c in cnts]
    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
        key=lambda b:b[1][i], reverse=reverse))
    # return the list of sorted contours and bounding boxesreturn (cnts, boundingBoxes)




#################################################################################################################################### https://medium.com/coinmonks/a-box-detection-algorithm-for-any-image-containing-boxes-756c15d7ed26    (with a few modifications)###################################################################################################################################defbox_extraction(img_for_box_extraction_path, cropped_dir_path):
    img = cv2.imread(img_for_box_extraction_path, 0)  # Read the image
    (thresh, img_bin) = cv2.threshold(img, 128, 255,
                                      cv2.THRESH_BINARY | cv2.THRESH_OTSU)  # Thresholding the image
    img_bin = 255-img_bin  # Invert the imagecv2.imwrite("Image_bin.jpg",img_bin)# Defining a kernel length
    kernel_length = np.array(img).shape[1]//200# A verticle kernel of (1 X kernel_length), which will detect all the verticle lines from the image.
    verticle_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, kernel_length))
    # A horizontal kernel of (kernel_length X 1), which will help to detect all the horizontal line from the image.
    hori_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_length, 1))
    # A kernel of (3 X 3) ones.
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))# Morphological operation to detect verticle lines from an image
    img_temp1 = cv2.erode(img_bin, verticle_kernel, iterations=3)
    verticle_lines_img = cv2.dilate(img_temp1, verticle_kernel, iterations=3)
    #cv2.imwrite("verticle_lines.jpg",verticle_lines_img)# Morphological operation to detect horizontal lines from an image
    img_temp2 = cv2.erode(img_bin, hori_kernel, iterations=3)
    horizontal_lines_img = cv2.dilate(img_temp2, hori_kernel, iterations=3)
    #cv2.imwrite("horizontal_lines.jpg",horizontal_lines_img)# Weighting parameters, this will decide the quantity of an image to be added to make a new image.
    alpha = 0.5
    beta = 1.0 - alpha
    # This function helps to add two image with specific weight parameter to get a third image as summation of two image.
    img_final_bin = cv2.addWeighted(verticle_lines_img, alpha, horizontal_lines_img, beta, 0.0)
    img_final_bin = cv2.erode(~img_final_bin, kernel, iterations=2)
    (thresh, img_final_bin) = cv2.threshold(img_final_bin, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)# For Debugging# Enable this line to see verticle and horizontal lines in the image which is used to find boxes#cv2.imwrite("img_final_bin.jpg",img_final_bin)# Find contours for image, which will detect all the boxes
    contours, hierarchy = cv2.findContours(
        img_final_bin, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    # Sort all the contours by top to bottom.
    (contours, boundingBoxes) = sort_contours(contours, method="top-to-bottom")
    idx = 0for c in contours:
        # Returns the location and width,height for every contour
        x, y, w, h = cv2.boundingRect(c)# If the box height is greater then 20, widht is >80, then only save it as a box in "cropped/" folder.if (w > 50and h > 20):# and w > 3*h:
            idx += 1
            new_img = img[y:y+h, x:x+w]
            cv2.imwrite(cropped_dir_path+str(x)+'_'+str(y) + '.png', new_img)


###########################################################################################################################################################defprepare_cropped_folder():
   p=pathlib.Path('./Cropped')
   if p.exists():   # Cropped folder non empty. Let's clean up
      files = [x for x in p.glob('*.*') if x.is_file()]
      for f in files:
         f.unlink()
   else:
      p.mkdir()

############################################################################################################################################################ MAIN###########################################################################################################################################################
prepare_cropped_folder()

# Read image from which text needs to be extracted 
img = cv2.imread("dkesg.png") 

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
  
# Performing OTSU threshold 
ret, thresh1 = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV) 

thresh1=255-thresh1
bin_y=np.zeros(thresh1.shape[0])

for x inrange(0,len(bin_y)):
    bin_y[x]=sum(thresh1[x,:])

bin_y=bin_y/max(bin_y)

ry=np.where(bin_y>0.995)[0]

for i inrange(0,len(ry)):
   cv2.line(img, (0, ry[i]), (thresh1.shape[1], ry[i]), (0, 0, 0), 1)

# We need to draw abox around the picture with a white border in order for box_detection to work
cv2.line(img,(0,0),(0,img.shape[0]-1),(255,255,255),2)
cv2.line(img,(img.shape[1]-1,0),(img.shape[1]-1,img.shape[0]-1),(255,255,255),2)
cv2.line(img,(0,0),(img.shape[1]-1,0),(255,255,255),2)
cv2.line(img,(0,img.shape[0]-1),(img.shape[1]-1,img.shape[0]-1),(255,255,255),2)

cv2.line(img,(0,0),(0,img.shape[0]-1),(0,0,0),1)
cv2.line(img,(img.shape[1]-3,0),(img.shape[1]-3,img.shape[0]-1),(0,0,0),1)
cv2.line(img,(0,0),(img.shape[1]-1,0),(0,0,0),1)
cv2.line(img,(0,img.shape[0]-2),(img.shape[1]-1,img.shape[0]-2),(0,0,0),1)


cv2.imwrite('out.png',img)
box_extraction("out.png", "./Cropped/")

Now... It puts the cropped regions in the Cropped folder. They are named as x_y.png with (x,y) the position on the original image.

Here are two examples of the outputs

and

Now, in a terminal. I used pytesseract on these two images.

The results are the following:

1)

Original Cost

$200,000.00

2)

Amount Existing Liens

$494,215.00

As you can see, pytesseract got the amount wrong in the second case... So, be careful.

Best regards, Stéphane

Solution 2:

I assume the bounding box is fix (rectangle that able to fit in "Original Amount and the amount below). You can use text detection to detect the "Original Amount" and "Amount Existing Liens" using OCR and crop out the image based on the detected location for further OCR on the amount. You can refer this link for text detection

Solution 3:

Try to divide the image into different cells using the lines in the image.

For example, first divide the input into rows by detecting the horizontal lines. This can be done by using cv.HoughLinesP and checking for each line if the difference between y-coordinate of the begin and end point is smaller than a certain threshold abs(y2 - y1) < 10. If you have a horizontal line, it's a separator for a new row. You can use the y-coordinates of this line to split the input horizontally.

Next, for the row you're interested in, divide the region into columns using the same technique, but now make sure the difference between the x-coordinates of the begin and end point are smaller than a certain threshold, since you're now looking for the vertical lines.

You can now crop the image to different cells using the y-coordinates of the horizontal lines and the x-coordinates of the vertical lines. Pass these cropped regions one by one to the OCR engine and you'll have for each cell the corresponding text.