Python - Image Detection of Digits

I want that the first picture are cutting like the second.uncut
cut
It is possible with all sort of dimensions of the image? It has to work for every image size also we don't know where the first pixel black is? It is used for a projet machine learning. We need the image cut for machine learning digit recognition.

from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
#couper l'image (entre le 1e et le dernier pixel)
def cuttingImage(image):
# ouverture d’une image entant que noir/blanc:
imageSource=Image.open(image).convert('L')
#dimension de l'image
width, height = imageSource.size
#test
#print(self.width,self.height)
#prendre tous les pixels d'une image dans un array
#diviser par 255 pour avoir 0 ou 1 comme valeur des pixels
pixels=np.asarray(imageSource,dtype=np.float32)/255
print("matriz de l'image",image)
print(pixels)
#créer un variable imageArray pour le tableau des pixels
#plus lisible
imageArray=pixels
#longeur maximum
maxWidth=width-1
#une valeur maximal entre les dimensions de l'image diviser par 20
#utilser pour couper l'image
space=max(width//20,height//20)
#rechercher le premier pixel
for top, row in enumerate(imageArray):
for left, pix in enumerate(row):
if pix>=0.7:
break
#recherche le dernier pixel
for down, row in enumerate(reversed(imageArray)):
for right, pix in enumerate(reversed(row)):
if maxWidth - right == left:
# Image impossible
break        
elif pix>=0.7:
break
#longeur de l'image
crop_width = abs(right - left) 
# hauteur de l'image
crop_height = abs(down - top) 
#premier pixel sur l'axe des abscisses
crop_center_x = int(left + crop_width/2)
#premier pixel sur l'axe de l'ordonné
crop_center_y = int(top + crop_height/2) 
#tester
#print(space)
#definier les dimensions du nouveau image
if(width<height):
if(space>=50 and space<60):
box=(((width-crop_center_x//2)//2,height-crop_center_y+3*space,crop_width-1.5*space,height+crop_height-3*space))
elif(space<200):
box=((-space+(width-crop_center_x//2)//2,height-crop_center_y+space,crop_width-space,height+crop_height-space))
else:
box=((-space+(width-crop_center_x//2)//2,height-crop_center_y+8*space,crop_width-1.5*space,height+crop_height-8*space))
else:
box=((-2*space+width-crop_center_x//2,height-crop_center_y+2*space,crop_width-3.5*space,height+crop_height-2*space))
#Couper l'image avec l'aide de variable box
imageSource=imageSource.crop(box)
imageSource.save("ImageNorm_img/new-test.jpg")
#fermer l'image
imageSource.close()

I think you just want something like this.

#!/usr/bin/env python3
from PIL import Image
import numpy as np
# Load image, and make into Numpy array
im = Image.open('text.jpg')
na = np.array(im.convert('L'))
# Stretch the contrast to range 0..255 to maximize chances of splitting digits from background
na = ((na.astype(np.float)-na.min())*255.0/(na.max()-na.min())).astype(np.uint8)
# Threshold image to pure black and white
blk = np.array([0],  np.uint8)
wht = np.array([255],np.uint8)
thr = np.where(na>128, blk, wht)
# Go back to PIL Image from Numpy array
res = Image.fromarray(thr)
# Get bounding box from thresholded image
bbox = res.getbbox()
print('Bounding box:',bbox)
# Apply bounding box to original image and save
result = im.crop(bbox)
result.save('result.png')