feat: Added WordGenerator dataset

[fg-mindee]

This PR introduces the following modifications:

• implements a WordGenerator in the same spirit as the CharGenerator using a min_chars and max_chars to specify the word length.
• updates unittests and documentation

To illustrate the PR, the following snippet:

from doctr.datasets.generator.base import synthesize_text_img
import matplotlib.pyplot as plt

plt.imshow(synthesize_text_img('a', font_size=32)); plt.show()
plt.imshow(synthesize_text_img('abcdefgh', font_size=32)); plt.show()

produces:

And the same in font_size=64:

Finally, the dataset itself:

from doctr.datasets import WordGenerator, VOCABS
import matplotlib.pyplot as plt
from math import floor

# Font will be picked randomly in the list
ds = WordGenerator(VOCABS['french'], 1, 10, num_samples=8, font_family=["FreeMono.ttf", "FreeSans.ttf", "FreeSerif.ttf"])
# Visualize
_, axes = plt.subplots(4, 2)
for idx in range(8):
    img, target = ds[idx]
    row_idx = int(idx / 2)
    col_idx = idx % 2
    axes[row_idx, col_idx].imshow(img.numpy())
    axes[row_idx, col_idx].set_title(target)
for ax in axes.ravel():
   ax.axis('off')
plt.show()

renders as follows:

(Easily turned into something more appealing with transformations like ColorInversion, etc.)

Please note that some characters such as bitcoin symbol are not properly rendered silently, this will have to be investigated

Closes #262

[codecov]

Codecov Report

Merging #760 (5455a2c) into main (56a5830) will decrease coverage by 0.16%.
The diff coverage is 83.72%.

@@            Coverage Diff             @@
##             main     #760      +/-   ##
==========================================
- Coverage   96.22%   96.05%   -0.17%     
==========================================
  Files         129      129              
  Lines        4764     4794      +30     
==========================================
+ Hits         4584     4605      +21     
- Misses        180      189       +9     

Flag	Coverage Δ
unittests	96.05% <83.72%> (-0.17%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
doctr/datasets/generator/__init__.py	100.00% <ø> (ø)
doctr/datasets/generator/base.py	81.08% <79.41%> (ø)
doctr/datasets/__init__.py	100.00% <100.00%> (ø)
doctr/datasets/generator/pytorch.py	100.00% <100.00%> (ø)
doctr/datasets/generator/tensorflow.py	100.00% <100.00%> (ø)
doctr/models/builder.py	96.52% <0.00%> (-1.74%)	⬇️

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[charlesmindee]

Thanks for the PR, indeed we need to find a way to filter out characters that cannot be rendered, an option could be to cast them with unidecode