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metadata
title: YuzuMarker.FontDetection
emoji: 😅
colorFrom: blue
colorTo: yellow
sdk: docker
app_port: 7860

YuzuMarker.FontDetection

Scene Text Font Dataset Generation

This repository also contains data for automatically generating a dataset of scene text images with different fonts. The dataset is generated using the CJK font pack by VCB-Studio and thousands of background image from pixiv.net.

The pixiv data will not be shared since it is just randomly scraped. You may prepare your own background dataset that would fit your data distribution as you like.

For the text corpus,

All text are also mixed with English text to simulate real-world data.

Data Preparation Walkthrough

  1. Download the CJK font pack and extract it to the dataset/fonts directory.
  2. Prepare the background data and put them in the dataset/pixivimages directory.
  3. Run following script to clean the file names
    python dataset_filename_preprocess.py

Generation Script Walkthrough

Now the preparation is complete. The following command can be used to generate the dataset:

python font_ds_generate_script.py 1 1

Note that the command is followed by two parameters. The second one is to split the task into multiple partitions, and the first one is the index of the partitioned task to run. For example, if you want to run the task in 4 partitions, you can run the following commands in parallel to speed up the process:

python font_ds_generate_script.py 1 4
python font_ds_generate_script.py 2 4
python font_ds_generate_script.py 3 4
python font_ds_generate_script.py 4 4

The generated dataset will be saved in the dataset/font_img directory.

Note that batch_generate_script_cmd_32.bat and batch_generate_script_cmd_64.bat are batch scripts for Windows that can be used to generate the dataset in parallel with 32 partitions and 64 partitions.

Final Check

Since the task might be terminated unexpectedly or deliberately by user. The script has a caching mechanism to avoid re-generating the same image.

In this case, the script might not be able to detect corruption in cache (might be caused by terminating when writing to files) during this task, thus we also provides a script checking the generated dataset and remove the corrupted images and labels.

python font_ds_detect_broken.py

After running the script, you might want to rerun the generation script to fill up the holes of the removed corrupted files.

(Optional) Linux Cluster Generation Walkthrough

If you would like to run the generation script on linux clusters, we also provides the environment setup script linux_venv_setup.sh.

The prerequisite is that you have a linux cluster with python3-venv installed and python3 is available in the path.

To setup the environment, run the following command:

./linux_venv_setup.sh

The script will create a virtual environment in the venv directory and install all the required packages. The script is required in most cases since the script will also install libraqm which is required for the text rendering of PIL and is often not installed by default in most linux server distributions.

After the environment is setup, you might compile a task scheduler to deploy generation task in parallel.

The main idea is similar to the direct usage of the script, except that here we accept three parameters,

  • TOTAL_MISSION: the total number of partitions of the task
  • MIN_MISSION: the minimum partition index of the task to run
  • MAX_MISSION: the maximum partition index of the task to run

and the compilation command is as following:

gcc -D MIN_MISSION=<MIN_MISSION> \
    -D MAX_MISSION=<MAX_MISSION> \
    -D TOTAL_MISSION=<TOTAL_MISSION> \
    batch_generate_script_linux.c \
    -o <object-file-name>.out

For example if you want to run the task in 64 partitions, and want to spilit the work on 4 machines, you can compile the following command on each machine:

# Machine 1
gcc -D MIN_MISSION=1 \
    -D MAX_MISSION=16 \
    -D TOTAL_MISSION=64 \
    batch_generate_script_linux.c \
    -o mission-1-16.out
# Machine 2
gcc -D MIN_MISSION=17 \
    -D MAX_MISSION=32 \
    -D TOTAL_MISSION=64 \
    batch_generate_script_linux.c \
    -o mission-17-32.out
# Machine 3
gcc -D MIN_MISSION=33 \
    -D MAX_MISSION=48 \
    -D TOTAL_MISSION=64 \
    batch_generate_script_linux.c \
    -o mission-33-48.out
# Machine 4
gcc -D MIN_MISSION=49 \
    -D MAX_MISSION=64 \
    -D TOTAL_MISSION=64 \
    batch_generate_script_linux.c \
    -o mission-49-64.out

Then you can run the compiled object file on each machine to start the generation task.

./mission-1-16.out # Machine 1
./mission-17-32.out # Machine 2
./mission-33-48.out # Machine 3
./mission-49-64.out # Machine 4

There is also another helper script to check the progress of the generation task. It can be used as following:

python font_ds_stat.py

MISC Info of the Dataset

The generation is CPU bound, and the generation speed is highly dependent on the CPU performance. Indeed the work itself is an engineering problem.

Some fonts are problematic during the generation process. The script has an manual exclusion list in config/fonts.yml and also support unqualified font detection on the fly. The script will automatically skip the problematic fonts and log them for future model training.

Font Classification Experiment Results

On our synthesized dataset,

Backbone Data Aug Pretrained Crop
Text
BBox		 Preserve
Aspect
Ratio		 Output
Norm		 Input Size Hyper
Param		 Accur Commit Dataset Precision
DeepFont ✔️* ❌ ✅ ❌ Sigmoid 105x105 I1 [Can't Converge] 665559f I5 bfloat16_3x
DeepFont ✔️* ❌ ✅ ❌ Sigmoid 105x105 IV4 [Can't Converge] 665559f I bfloat16_3x
ResNet-18 ❌ ❌ ❌ ❌ Sigmoid 512x512 I 18.58% 5c43f60 I float32
ResNet-18 ❌ ❌ ❌ ❌ Sigmoid 512x512 II2 14.39% 5a85fd3 I bfloat16_3x
ResNet-18 ❌ ❌ ❌ ❌ Tanh 512x512 II 16.24% ff82fe6 I bfloat16_3x
ResNet-18 ✅*7 ❌ ❌ ❌ Tanh 512x512 II 27.71% a976004 I bfloat16_3x
ResNet-18 ✅* ❌ ❌ ❌ Tanh 512x512 I 29.95% 8364103 I bfloat16_3x
ResNet-18 ✅* ❌ ❌ ❌ Sigmoid 512x512 I 29.37% [Early stop] 8d2e833 I bfloat16_3x
ResNet-18 ✅* ❌ ❌ ❌ Sigmoid 416x416 I [Lower Trend] d5a3215 I bfloat16_3x
ResNet-18 ✅* ❌ ❌ ❌ Sigmoid 320x320 I [Lower Trend] afcdd80 I bfloat16_3x
ResNet-18 ✅* ❌ ❌ ❌ Sigmoid 224x224 I [Lower Trend] 8b9de80 I bfloat16_3x
ResNet-34 ✅* ❌ ❌ ❌ Sigmoid 512x512 I 32.03% 912d566 I bfloat16_3x
ResNet-50 ✅* ❌ ❌ ❌ Sigmoid 512x512 I 34.21% e980b66 I bfloat16_3x
ResNet-18 ✅* ✅ ❌ ❌ Sigmoid 512x512 I 31.24% 416c7bb I bfloat16_3x
ResNet-18 ✅* ✅ ✅ ❌ Sigmoid 512x512 I 34.69% 855e240 I bfloat16_3x
ResNet-18 ✔️*8 ✅ ✅ ❌ Sigmoid 512x512 I 38.32% 1750035 I bfloat16_3x
ResNet-18 ✔️* ✅ ✅ ❌ Sigmoid 512x512 III3 38.87% 0693434 I bfloat16_3x
ResNet-50 ✔️* ✅ ✅ ❌ Sigmoid 512x512 III 48.99% bc0f7fc II6 bfloat16_3x
ResNet-50 ✔️ ✅ ✅ ✅10 Sigmoid 512x512 III 46.12% 0f071a5 II bfloat16
ResNet-50 ❕9 ✅ ✅ ❌ Sigmoid 512x512 III 43.86% 0f071a5 II bfloat16
ResNet-50 ❕ ✅ ✅ ✅ Sigmoid 512x512 III 41.35% 0f071a5 II bfloat16
  • * Bug in implementation
  • 1 learning rate = 0.0001, lambda = (2, 0.5, 1)
  • 2 learning rate = 0.00005, lambda = (4, 0.5, 1)
  • 3 learning rate = 0.001, lambda = (2, 0.5, 1)
  • 4 learning rate = 0.01, lambda = (2, 0.5, 1)
  • 5 Initial version of synthesized dataset
  • 6 Doubled synthesized dataset
  • 7 Data Augmentation v1: Color Jitter + Random Crop [81%-100%]
  • 8 Data Augmentation v2: Color Jitter + Random Crop [30%-130%] + Random Gaussian Blur + Random Gaussian Noise + Random Rotation [-15°, 15°]
  • 9 Data Augmentation v3: Color Jitter + Random Crop [30%-130%] + Random Gaussian Blur + Random Gaussian Noise + Random Rotation [-15°, 15°] + Random Horizontal Flip + Random Downsample [1, 2]
  • 10 Preserve Aspect Ratio by Random Cropping

Related works and Resources