Using mouseevent. It is straight forward. It will save iterator files

1. Drag rectangle
2. Press "s" to save
3. Press "r" to rest
4. Do step 1 to 3
5. Press "c" to exit.

Code:

import cv2 as cv

refPt = []
cropping = False

def click_crop(event, x, y, flags, param):
    global refPt, cropping, num
    if event == cv.EVENT_LBUTTONDOWN:
        refPt = [(x, y)]
        cropping = True

    if event == cv.EVENT_LBUTTONUP:
        num += 1
        refPt.append((x, y))
        cropping = False        
        cv.rectangle(img, refPt[0], refPt[1], (0, 255, 0), 2)

if __name__ == "__main__":
    num = 0

    windowName = 'Click and Crop'
    img = cv.imread('text.png', cv.IMREAD_COLOR)
    clone = img.copy()
    cv.namedWindow(windowName)

    cv.setMouseCallback(windowName, click_crop)

    num = 0
    if len(refPt) == 2:
        num += 1

    while True:
        cv.imshow(windowName, img)
        key = cv.waitKey(1)
        if key == ord("r"): 
            img = clone.copy()
        elif key == ord("s"):   
            roi = clone[refPt[0][1]:refPt[1][1], refPt[0][0]:refPt[1][0]]
            cv.imwrite('roi{}.jpg'.format(num), roi)            
        elif key == ord("c"):  
            break    
    cv.destroyAllWindows()

Output:

Using mouseevent. It is straight forward. It will save iterator files

1. Drag rectangle
2. Press "s" to save
3. Press "r" to rest
4. Do step 1 to 3
5. Press "c" to exit.

Code:

import cv2 as cv

refPt = []
cropping = False

def click_crop(event, x, y, flags, param):
    global refPt, cropping, num
    if event == cv.EVENT_LBUTTONDOWN:
        refPt = [(x, y)]
        cropping = True

    if event == cv.EVENT_LBUTTONUP:
        num += 1
        refPt.append((x, y))
        cropping = False        
        cv.rectangle(img, refPt[0], refPt[1], (0, 255, 0), 2)

if __name__ == "__main__":
    num = 0

    windowName = 'Click and Crop'
    img = cv.imread('text.png', cv.IMREAD_COLOR)
    clone = img.copy()
    cv.namedWindow(windowName)

    cv.setMouseCallback(windowName, click_crop)

    num = 0
    if len(refPt) == 2:
        num += 1

    while True:
        cv.imshow(windowName, img)
        key = cv.waitKey(1)
        if key == ord("r"): 
            img = clone.copy()
        elif key == ord("s"):   
            roi = clone[refPt[0][1]:refPt[1][1], refPt[0][0]:refPt[1][0]]
            cv.imwrite('roi{}.jpg'.format(num), roi)            
        elif key == ord("c"):  
            break    
    cv.destroyAllWindows()

Output:

import cv2

image = cv2.imread('text.png')
#image = cv2.imread('text4.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5,5), 0)
thresh = cv2.threshold(blur, 127, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Fill contours
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
morph = 255 - cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=3)
#morph = 255 - cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=6)

close = 255 - morph 
opening = cv2.morphologyEx(close, cv2.MORPH_OPEN, kernel, iterations=3)

# Busca los contornos y dibuja los resultados
ROI_number = 0
cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    ROI = image[y:y+h, x:x+w]

    cv2.rectangle(image,(x,y),(x+w,y+h),(36,255,12),2)
    #cv2.imwrite('images_{}.png'.format(ROI_number), ROI)
    cv2.imwrite('text3.jpg', image)
    ROI_number += 1 

print("Cantidad de entidades ", len(cnts))
cv2.imshow('thresh', thresh)
cv2.imshow('opening', blur)
cv2.imshow('image', image)
cv2.waitKey()

Using mouseevent. It is straight forward. It will save iterator files

1. Drag rectangle
2. Press "s" to save
3. Press "r" to rest
4. Do step 1 to 3
5. Press "c" to exit.

Code:

import cv2 as cv

refPt = []
cropping = False

def click_crop(event, x, y, flags, param):
    global refPt, cropping, num
    if event == cv.EVENT_LBUTTONDOWN:
        refPt = [(x, y)]
        cropping = True

    if event == cv.EVENT_LBUTTONUP:
        num += 1
        refPt.append((x, y))
        cropping = False        
        cv.rectangle(img, refPt[0], refPt[1], (0, 255, 0), 2)

if __name__ == "__main__":
    num = 0

    windowName = 'Click and Crop'
    img = cv.imread('text.png', cv.IMREAD_COLOR)
    clone = img.copy()
    cv.namedWindow(windowName)

    cv.setMouseCallback(windowName, click_crop)

    num = 0
    if len(refPt) == 2:
        num += 1

    while True:
        cv.imshow(windowName, img)
        key = cv.waitKey(1)
        if key == ord("r"): 
            img = clone.copy()
        elif key == ord("s"):   
            roi = clone[refPt[0][1]:refPt[1][1], refPt[0][0]:refPt[1][0]]
            cv.imwrite('roi{}.jpg'.format(num), roi)            
        elif key == ord("c"):  
            break    
    cv.destroyAllWindows()

Output:

@Agustin. Don't used cv2.drawContours. You get both rectangles. I am not going to post screenshot for cropped. I merely got 2 images. Second images contained purple rectangle and 3rd image contained heavy grey border. I am running out of my time. I will attempt to get it back sooner.

import cv2

image = cv2.imread('text.png')
#image = cv2.imread('text4.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5,5), 0)
thresh = cv2.threshold(blur, 127, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Fill contours
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
morph = 255 - cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=3)
#morph = 255 - cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=6)

close = 255 - morph 
opening = cv2.morphologyEx(close, cv2.MORPH_OPEN, kernel, iterations=3)

# Busca los contornos y dibuja los resultados
ROI_number = 0
cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    ROI = image[y:y+h, x:x+w]

    cv2.rectangle(image,(x,y),(x+w,y+h),(36,255,12),2)
    #cv2.imwrite('images_{}.png'.format(ROI_number), cv2.imwrite('images_{}.png'.format(ROI_number), ROI)
    cv2.imwrite('text3.jpg', image)
    ROI_number += 1 

print("Cantidad de entidades ", len(cnts))
cv2.imshow('thresh', thresh)
cv2.imshow('opening', blur)
cv2.imshow('image', image)
cv2.waitKey()

Output: for text:

Output for text4 using iterations=6: