Filtering image in opencv with histogram projection - Mask problem

I am making an algorithm to detect license plates, specifically at character segmentation part. For this I use a histogram projection.

My idea is to use this histogram projection to

1. Clean edges
2. Segment the characters

The problem is when I want to apply histogram projection twice, when applying a filter, it does not show well (see last image)

Example with clean edges, with this binary image:

I have this algorithm that cleans the edges of a binary image:

from matplotlib import pyplot as plt
import pylab
import numpy as np

img = binary_image # input image
(rows,cols)=img.shape

h_projection = np.array([ x/rows for x in img.sum(axis=0)])
threshold = 255 - 60
print("we will use threshold {} for horizontal".format(threshold))
# select the black areas
black_areas = np.where(h_projection > threshold)
fig = plt.figure(figsize=(16,8))
fig.add_subplot(121)
for j in black_areas:
    img[:, j] = 0
    plt.plot((j, j), (0, 1), 'g-')
plt.plot(range(cols), h_projection.T)

v_projection = np.array([ x/cols for x in img.sum(axis=1)])
threshold = 255 - 60
print("we will use threshold {} for vertical".format(threshold))
black_areas = np.where(v_projection > threshold)
fig.add_subplot(122)
for j in black_areas:
    img[j, :] = 0
    plt.plot((0,1), (j,j), 'g-')
plt.plot(v_projection, range(rows))
plt.show()

# obscurate areas on the image
plt.figure(figsize=(16,12))
plt.subplot(211)
plt.title("Image with the projection mask")
plt.imshow(img)

And the output:

It do it very well (It could be more specific with the threshold, but I can't because different images were loaded for a neural network).

Now if I want to apply another histogram projection for character segmentation:

from matplotlib import pyplot as plt
import pylab
import numpy as np

#input image will be the same img variable.

(rows,cols)=img.shape

h_projection = np.array([ x/rows for x in img.sum(axis=0)])
print(np.min(h_projection))
print(np.max(h_projection))
threshold = (np.max(h_projection) - np.min(h_projection)) / 4
print("we will use threshold {} for horizontal".format(threshold))
# select the black areas
black_areas = np.where(h_projection < threshold)
fig = plt.figure(figsize=(16,8))
fig.add_subplot(121)
for j in black_areas:
    img[:, j] = 1
    plt.plot((j, j), (0, 1), 'g-')
plt.plot(range(cols), h_projection.T)

v_projection = np.array([ x/cols for x in img.sum(axis=1)])
threshold = (np.max(v_projection) - np.min(v_projection)) / 4
print("we will use threshold {} for vertical".format(threshold))
black_areas = np.where(v_projection < threshold)
fig.add_subplot(122)
for j in black_areas:
    img[j, :] = 0
    plt.plot((0,1), (j,j), 'g-')
plt.plot(v_projection, range(rows))
plt.show()

# obscurate areas on the image
plt.figure(figsize=(16,12))
plt.subplot(211)
plt.title("Image with the projection mask")
plt.imshow(img)

# erode the features
import scipy
plt.subplot(212)
plt.title("Image after erosion (suggestion)")
eroded_img = scipy.ndimage.morphology.binary_erosion(img,  structure=np.ones((1,1))).astype(img.dtype)
plt.imshow(eroded_img)
plt.show()

It will show something like this:

As you can see the last image, when erosion it's applied, it seems that don't take the black of the previous the mask. Am I marking some areas wrong?