Do you know of an algorithm that can see that there is handwriting on an image? I am not interested in knowing what the handwriting says, but only that there is one present?

I have a video of someone filling a slide with handwriting. My goal is to determine how much of the slide has been filled with handwriting already.

The video in question can be downloaded here: http://www.filedropper.com/00_6

For this particular video, a great solution was already suggested in Quantify how much a slide has been filled with handwriting

The solution is based on summing the amount of the specific color used for the handwriting. However, if the handwriting is not in blue but any other color that can also be found on non-handwriting, this approach will not work.

Therefore, I am interested to know, if there exists a more general solution to determine if there is handwriting present on an image?

What I have done so far: I was thinking of extracting the contours of an image, and then somehow detect the handwriting part based on how curvy the contours are (but I have no clue how to do that part). it might not be the best idea, though, as again it's not always correct...

import cv2
import matplotlib.pyplot as pltimg = cv2.imread(PATH TO IMAGE)
print("img shape=", img.shape)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)cv2.imshow("image", gray)
cv2.waitKey(1)
#### extract all contours
# Find Canny edges 
edged = cv2.Canny(gray, 30, 200) 
cv2.waitKey(0) # Finding Contours 
# Use a copy of the image e.g. edged.copy() 
# since findContours alters the image 
contours, hierarchy = cv2.findContours(edged,  cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) cv2.imshow('Canny Edges After Contouring', edged) 
cv2.waitKey(0) print("Number of Contours found = " + str(len(contours))) # Draw all contours 
# -1 signifies drawing all contours 
cv2.drawContours(img, contours, -1, (0, 255, 0), 3) cv2.imshow('Contours', img) 
cv2.waitKey(0) 

You can identify the space taken by hand-writing by masking the pixels from the template, and then do the same for the difference between further frames and the template. You can use dilation, opening, and thresholding for this.

Let's start with your template. Let's identify the parts we will mask:

import cv2
import numpy as nptemplate = cv2.imread('template.jpg')

Now, let's broaden the occupied pixels to make a zone that we will mask (hide) later:

template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
kernel = np.ones((5, 5),np.uint8)
dilation = cv2.dilate(255 - template, kernel,iterations = 5)

Then, we will threshold to turn this into a black and white mask:

_, thresh = cv2.threshold(dilation,25,255,cv2.THRESH_BINARY_INV)

In later frames, we will subtract this mask from the picture, by turning all these pixels to white. For instance:

import numpy as np
import cv2
vidcap = cv2.VideoCapture('0_0.mp4')
success,image = vidcap.read()
count = 0
frames = []while count < 500:frames.append(image)success,image = vidcap.read()count += 1mask = np.where(thresh == 0)example = frames[300]
example[mask] = [255, 255, 255]
cv2.imshow('', example)
cv2.waitKey(0)

Now, we will create a function that will return the difference between the template and a given picture. We will also use opening to get rid of the left over single pixels that would make it ugly.

def difference_with_mask(image):grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)kernel = np.ones((5, 5), np.uint8)dilation = cv2.dilate(255 - grayscale, kernel, iterations=5)_, thresh = cv2.threshold(dilation, 25, 255, cv2.THRESH_BINARY_INV)thresh[mask] = 255closing = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)return closingcv2.imshow('', difference_with_mask(frames[400]))
cv2.waitKey(0)

To address the fact that you don't want to have the hand detected as hand-writing, I suggest that instead of using the mask for every individual frame, you use the 95th percentile of the 15 last 30th frame... hang on. Look at this:

results = []
for ix, frame in enumerate(frames):if ix % 30 == 0:history.append(frame)results.append(np.quantile(history, 0.95, axis=0))print(ix)

Now, the example frame becomes this (the hand is removed because it wasn't mostly present in the 15 last 30th frames):

As you can see a little part of the hand-writing is missing. It will come later, because of the time-dependent percentile transformation we're doing. You'll see later: in my example with frame 18,400, the text that is missing in the image above is present. Then, you can use the function I gave you and this will be the result:

And here we go! Note that this solution, which doesn't include the hand, will take longer to compute because there's a few calculations needing to be done. Using just an image with no regard to the hand would calculate instantly, to the extent that you could probably run it on your webcam feed in real time.

Final Example:

Here's the frame 18,400:

Final image:

You can play with the function if you want the mask to wrap more thinly around the text:

Full code:

import os
import numpy as np
import cv2
vidcap = cv2.VideoCapture('0_0.mp4')
success,image = vidcap.read()
count = 0
from collections import deque
frames = deque(maxlen=700)while count < 500:frames.append(image)success,image = vidcap.read()count += 1template = cv2.imread('template.jpg')
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
kernel = np.ones((5, 5),np.uint8)
dilation = cv2.dilate(255 - template, kernel,iterations = 5)cv2.imwrite('dilation.jpg', dilation)
cv2.imshow('', dilation)
cv2.waitKey(0)_, thresh = cv2.threshold(dilation,25,255,cv2.THRESH_BINARY_INV)
cv2.imwrite('thresh.jpg', thresh)
cv2.imshow('', thresh)
cv2.waitKey(0)mask = np.where(thresh == 0)example = frames[400]
cv2.imwrite('original.jpg', example)
cv2.imshow('', example)
cv2.waitKey(0)example[mask] = 255
cv2.imwrite('example_masked.jpg', example)
cv2.imshow('', example)
cv2.waitKey(0)def difference_with_mask(image):grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)kernel = np.ones((5, 5), np.uint8)dilation = cv2.dilate(255 - grayscale, kernel, iterations=5)_, thresh = cv2.threshold(dilation, 25, 255, cv2.THRESH_BINARY_INV)thresh[mask] = 255closing = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)return closingcv2.imshow('', difference_with_mask(frames[400]))
cv2.waitKey(0)masked_example = difference_with_mask(frames[400])
cv2.imwrite('masked_example.jpg', masked_example)from collections import deque
history = deque(maxlen=15)results = []
for ix, frame in enumerate(frames):if ix % 30 == 0:history.append(frame)results.append(np.quantile(history, 0.95, axis=0))print(ix)if ix > 500:breakcv2.imshow('', frames[400])
cv2.waitKey(0)cv2.imshow('', results[400].astype(np.uint8))
cv2.imwrite('percentiled_frame.jpg', results[400].astype(np.uint8))
cv2.waitKey(0)cv2.imshow('', difference_with_mask(results[400].astype(np.uint8)))
cv2.imwrite('final.jpg', difference_with_mask(results[400].astype(np.uint8)))
cv2.waitKey(0)