• Overview
• Documentation
• System Requirements
• Installation Guide
• DCNN DropBlock Algorithm flow
• Dataset
• Results
• Visualization
• Credits
• Citation
• License

Kuzushiji-Dropblock is a Project based on Recognizition of Japanese Historical Image classification.

📝 View our research paper titled "Japanese Historical Character Recognition using Deep Convolutional Neural Network (DCNN) with DropBlock Regularization" availble at (http://dx.doi.org/10.35940/ijrte.b2923.078219)

🚀 Kuzushiji-DropBlock package requires only a standard computer with enough RAM to support the in-memory operations.

This code is supported for Windows, macOS and Linux. The code has been tested on the following systems:

• Windows: Professional (10)
• macOS: Mojave (10.14.1)
• Linux: Ubuntu 16.04

Kuzushiji-DropBlock mainly depends on the Python scientific stack.

Keras
numpy
scipy
pandas
matplotlib
scikit-learn
PyTorch
Tensorflow
DropBlock

In Bash,

pip install DropBlock2D

In Google Colab,

!pip install DropBlock2D

GitClone

git clone https://github.com/sujatasaini/Kuzushiji-DropBlock
cd Kuzushiji-DropBlock

📁 Kuzushiji-MNIST is a drop-in replacement for the MNIST dataset (28x28 grayscale, 70,000 images), provided in the original MNIST format as well as a NumPy format.

Kuzushiji-MNIST contains 70,000 28x28 grayscale images spanning 10 classes (one from each column of hiragana), and is perfectly balanced like the original MNIST dataset (6k/1k train/test for each class).

Mapping from class indices to characters: kmnist_classmap.csv (1KB)

it's recommended to download in NumPy format, which can be loaded into an array as easy as:
arr = np.load(filename)['arr_0'].

Kuzushiji-49, has 49 classes (28x28 grayscale, 270,912 images), is a much larger, but imbalanced dataset containing 48 Hiragana characters and one Hiragana iteration mark.

Kuzushiji-49 contains 270,912 images spanning 49 classes, and is an extension of the Kuzushiji-MNIST dataset.

Mapping from class indices to characters: k49_classmap.csv (1KB)

Kuzushiji-Kanji is an imbalanced dataset of total 3832 Kanji characters (64x64 grayscale, 140,426 images of both common and rare characters), ranging from 1,766 examples to only a single example per class.

The full dataset is available for download here (310MB).

💾 You can run python download_data.py to interactively select and download any of these datasets! You can also download the data from Kaggle and participate in Kaggle Kuzushiji Recognition Competition.

📈 Results of our DCNN Model with different regularization methods (DropBlock, Dropout, SpatialDropout) on MNIST, Fashion-MNIST Kuzushiji-MNIST and Kuzushiji-49, trained on Google Colab average over 3 runs.

Have more results to add to the table? Feel free to submit an issue or pull request!

📊 *Used dropblock with block_size=7 and keep_prob=0.8 over 100 iterations

Example available here

1. Keras-DropBlock 🍀
2. KMNIST Dataset 🌸

Please cite Kuzushiji-DropBlock in your publications if it helps your research::100:

@article{2019,
       doi = {10.35940/ijrte.b2923.078219},
       url = {https://doi.org/10.35940%2Fijrte.b2923.078219},
       year = 2019,
       month = {jul},
       publisher = {Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - {BEIESP}},
       volume = {8},
       number = {2},
       pages = {3510--3515},
       title = {Japanese Historical Character Recognition using Deep Convolutional Neural Network ({DCNN}) with Drop Block Regularization},
       journal = {International Journal of Recent Technology and Engineering}
}