Extracting hand writing text out in shape with OpenCV
Question: I am very new to OpenCV Python and I really need some help here.

So what I am trying to do here is to extract out these words in the image
below.

[![hand drawn
image](http://i.stack.imgur.com/XsSOP.jpg)](http://i.stack.imgur.com/XsSOP.jpg)

The words and shapes are all hand drawn, so they are not perfect. I have did
some coding below.

First of all, I grayscale the image

    
    
    img_final = cv2.imread(file_name)
    img2gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    

Then I use THRESH_INV to show the content

    
    
    ret, new_img = cv2.threshold(image_final, 100 , 255, cv2.THRESH_BINARY_INV)
    

After which, I dilate the content

    
    
    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS,(3 , 3)) 
    dilated = cv2.dilate(new_img,kernel,iterations = 3)
    

I dilate the image is because I can identify text as one cluster

After that, I apply boundingRect around the contour and draw around the
rectangle

    
    
    contours, hierarchy = cv2.findContours(dilated,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) # get contours
    index = 0
    for contour in contours:
    
        # get rectangle bounding contour
        [x,y,w,h] = cv2.boundingRect(contour)
    
        #Don't plot small false positives that aren't text
        if w < 10 or h < 10:
            continue
    
        # draw rectangle around contour on original image
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,255),2)
    

This is what I got after that.

[![result
image](http://i.stack.imgur.com/vxKYi.png)](http://i.stack.imgur.com/vxKYi.png)

I am only able to detect one of the text. I have tried many other methods but
this is the closet results I have got and it does not fulfill the requirement.

The reason for me to identify the text is so that I can get the X and Y
coordinate of each of the text in this image by putting a bounding Rectangle
"boundingRect()".

Please help me out. Thank you so much


Answer: You can use the fact that the connected component of the letters are much
smaller than the large strokes of the rest of the diagram.

I used opencv3 connected components in the code but you can do the same things
using findContours.

The code:

    
    
    import cv2
    import numpy as np
    
    # Params
    maxArea = 150
    minArea = 10
    
    # Read image
    I = cv2.imread('i.jpg')
    
    # Convert to gray
    Igray = cv2.cvtColor(I,cv2.COLOR_RGB2GRAY)
    
    # Threshold
    ret, Ithresh = cv2.threshold(Igray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
    
    # Keep only small components but not to small
    comp = cv2.connectedComponentsWithStats(Ithresh)
    
    labels = comp[1]
    labelStats = comp[2]
    labelAreas = labelStats[:,4]
    
    for compLabel in range(1,comp[0],1):
    
        if labelAreas[compLabel] > maxArea or labelAreas[compLabel] < minArea:
            labels[labels==compLabel] = 0
    
    labels[labels>0] =  1
    
    # Do dilation
    se = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(25,25))
    IdilateText = cv2.morphologyEx(labels.astype(np.uint8),cv2.MORPH_DILATE,se)
    
    # Find connected component again
    comp = cv2.connectedComponentsWithStats(IdilateText)
    
    # Draw a rectangle around the text
    labels = comp[1]
    labelStats = comp[2]
    #labelAreas = labelStats[:,4]
    
    for compLabel in range(1,comp[0],1):
    
        cv2.rectangle(I,(labelStats[compLabel,0],labelStats[compLabel,1]),(labelStats[compLabel,0]+labelStats[compLabel,2],labelStats[compLabel,1]+labelStats[compLabel,3]),(0,0,255),2)
    

[![enter image description
here](http://i.stack.imgur.com/k0UdG.jpg)](http://i.stack.imgur.com/k0UdG.jpg)

