Alright, so you got the data that is great!

Now, you'll just need to prepare the npz files. Preparing them is actually quite simple.
You'll need to create 4 numpy arrays:

an array that you call num_words. This array has only one element and is of type int. The element should be the max. number of characters per image (it is called num_words because the network thinks that each character is a word). In our experiments we always set this to 23.
an array with one element of type type int. You call this num_chars. Here the value of the element should be 1 because we have num_words words of one character during training.
an array called file_name of type string. Here, you concatenate the relative path to all image files that you want to use for evaluation.
another array of strings called text. This time with the word in each image. Make sure that the indices align. So the word at index 1 in the array text should correspond to the correct image file at index 1 in the array file_name.

Once you have all of these arrays, you just need to save them (let me show you an example):

# create the arrays
data = {
  "num_words": ....,
  "num_chars": ....,
  "file_name": ....,
  "text": ...
}

# now we save everything
 with open("destination.npz", 'wb') as f:
        numpy.savez_compressed(f, **data)

The data link is broken #21

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