I would like to use deep learning program for recognizing the captcha using keras with python.

But the big challenge is to generate massive captcha to train. I want to solve a captcha like this

How can i easily generate above massive captcha to train.

Currently, i use python package captcha

from captcha.image import ImageCaptcha  # pip install captcha
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import random
import osnumber = ['0','1','2','3','4','5','6','7','8','9']
MAX_CAPTCHA = 6
WIDTH=100
HEIGHT=30image = ImageCaptcha(width=WIDTH, height=HEIGHT, font_sizes=[30])captcha_text = []
for i in range(MAX_CAPTCHA):c = random.choice(number)captcha_text.append(c)#print(captcha_text)
captcha_text = ''.join(captcha_text)
print(captcha_text)captcha = image.generate(captcha_text)
captcha_image = Image.open(captcha)
captcha_image = np.array(captcha_image)image.write(captcha_text, str(i)+'_'+captcha_text + '.png') 
plt.imshow(captcha_image)
plt.show()  

If there is no existing similar captcha datasets online I would tackle this problem in the following way:

1. Get the MNIST dataset
2. Take one image example and play with it in gimp or some image transformation librari like Open CV, to get a look similar to your captcha examples.
3. Transform the MNIST examples in a way you find fit (some random noise, and randomized color on black pixels or something)
4. Train a model on these augmented examples

Now for practical use it depends a little what kind of model you are implementing. If you have a model that can detect and classify all the numbers on the image then you are finished. But if you like to have a simple model that classifies only images with a single digit on them, then you can move a sliding window over your captcha image and only collect outputs of windows for wich the model has high enough confidence that there is some number pressent in the window.