Advanced

Beyond basic usage, WordCanvas offers several advanced settings that allow you to flexibly control the output of text images. Here we introduce randomness settings, which are primarily used for training models.

Random Fonts

Enable the random font feature using the random_font parameter. When random_font is set to True, the font_bank parameter becomes active, while font_path is ignored.

You should specify the font_bank parameter to your font library since the default is the package's fonts directory. For demonstration, we've placed two fonts in the fonts directory, so if you do not modify font_bank, it will randomly select from these two fonts.

import numpy as np
from wordcanvas import WordCanvas

gen = WordCanvas(
    random_font=True,
    output_size=(64, 512),
    font_bank="path/to/your/font/bank"
)

imgs = []
for _ in range(8):
    text = 'Hello, World!'
    img, infos = gen(text)
    imgs.append(img)

# Combine all images for output
img = np.concatenate(imgs, axis=0)

Random Text Content

If you are unsure of what text to generate, you can use the random_text parameter.

When random_text is set to True, the originally input text will be ignored.

import numpy as np
from wordcanvas import WordCanvas

gen = WordCanvas(
    random_text=True,
    output_size=(64, 512),
)

imgs = []
for _ in range(8):
    text = 'Hello!World!' # This input will be ignored
    img, infos = gen(text)
    imgs.append(img)

# Combine all images for output
img = np.concatenate(imgs, axis=0)

Specifying Text Length

When random_text is enabled, you can use:

• min_random_text_length: Minimum text length
• max_random_text_length: Maximum text length

These two parameters specify the range of text lengths.

import numpy as np
from wordcanvas import WordCanvas

# Always generate text with 5 characters
gen = WordCanvas(
    random_text=True,
    min_random_text_length=5,
    max_random_text_length=5,
    output_size=(64, 512),
)

imgs = []
for _ in range(8):
    img, infos = gen()
    imgs.append(img)

# Combine all images for output
img = np.concatenate(imgs, axis=0)

Random Background Color

Use the random_background_color parameter to enable the random background color feature.

import numpy as np
from wordcanvas import WordCanvas

gen = WordCanvas(
    random_background_color=True,
    output_size=(64, 512),
)

imgs = []
for _ in range(8):
    text = 'Hello, World!'
    img, infos = gen(text)
    imgs.append(img)

# Combine all images for output
img = np.concatenate(imgs, axis=0)

Random Text Color

Use the random_text_color parameter to enable the random text color feature.

import numpy as np
from wordcanvas import WordCanvas

gen = WordCanvas(
    random_text_color=True,
    output_size=(64, 512),
)

imgs = []
for _ in range(8):
    text = 'Hello, World!'
    img, infos = gen(text)
    imgs.append(img)

# Combine all images for output
img = np.concatenate(imgs, axis=0)

Random Text Alignment

Use the random_align_mode parameter to enable the random text alignment feature.

import numpy as np
from wordcanvas import WordCanvas

gen = WordCanvas(
    random_align_mode=True,
    output_size=(64, 512),
)

imgs = []
for _ in range(8):
    text = 'Hello, World!'
    img, infos = gen(text)
    imgs.append(img)

# Combine all images for output
img = np.concatenate(imgs, axis=0)

Random Text Direction

Use the random_direction parameter to enable the random text direction feature.

It's recommended to use this parameter in conjunction with output_direction for convenient image output.

import numpy as np
from wordcanvas import WordCanvas, OutputDirection

gen = WordCanvas(
    random_direction=True,
    output_direction=OutputDirection.Horizontal,
    output_size=(64, 512),
)

imgs = []
for _ in range(8):
    text = '午安，或是晚安。'
    img, infos = gen(text)
    imgs.append(img)

# Combine all images for output
img = np.concatenate(imgs, axis=0)

Full Randomization

If you want all settings to be random, you can use the enable_all_random parameter.

This parameter activates a mode where everything is randomized.

import numpy as np
from wordcanvas import WordCanvas

gen = WordCanvas(
    enable_all_random=True,
    output_size=(64, 512),
)

imgs = []
for _ in range(20):
    img, infos = gen()
    imgs.append(img)

# Combine all images for output
img = np.concatenate(imgs, axis=0)

warning

This parameter does not adjust parameters that require reinitialization, such as random_font, random_text, etc. These need to be set manually.

Dashboard Revisited

Let's return to the dashboard feature.

When randomness-related parameters are enabled, parameters set to True will be marked in green, while those set to False will be marked in red.

We hope this design allows you to quickly verify related settings.

Font Weights

tip

This feature was introduced in version 0.2.0.

Since different fonts support varying numbers of characters, we may encounter issues with uneven font weights during model training.

To put it simply, the probability of randomly selecting each font is equal. However, some characters are only supported by a few fonts, meaning certain characters might rarely be trained on.

To mitigate this issue, we introduced the use_random_font_weight parameter.

import numpy as np
from wordcanvas import WordCanvas

gen = WordCanvas(
    random_font=True,
    use_random_font_weight=True,
    output_size=(64, 512),
)

When you enable this parameter, WordCanvas adjusts the font selection probability based on the number of characters each font supports. Fonts that support fewer characters will be less likely to be chosen, resulting in a more balanced distribution.

However, there's still room for improvement. We believe that by first calculating the frequency of all characters, and then assigning selection weights, we could achieve better results. We plan to release this feature in version 0.5.0.

Blocklist

tip

This feature was introduced in version 0.4.0.

We found that some fonts claim to support certain characters but fail to render them correctly.

For instance, a font file might list a set of supported characters, but in practice, some of those characters do not display properly.

This was quite frustrating, so we developed a blocklist feature that allows you to exclude such fonts by using the block_font_list parameter:

import numpy as np
from wordcanvas import WordCanvas

gen = WordCanvas(
    random_font=True,
    use_random_font_weight=True,
    block_font_list=['Blocked Font Name']
)

Summary

Our goal in developing this tool is to create a flexible solution for generating various text images, particularly for training machine learning models.

Introducing randomness is intended to simulate real-world scenarios, which greatly enhances the adaptability and generalization capabilities of models. We hope you find these features useful.