Create a model that recognizing handwritten Japanese characters, including Hiragana, Katakana, Kanji, and Kuzushiji, using Tensorflow.
Python 3.7 (any python3 versions should work)
Tensorflow 2.2.0
Keras 2.4.3 - note that this would need to be downgraded to 2.2.4 to convert h5 to mlmodel
Numpy 1.16.4 (this isn't the newest version but this prevents tensorflow from printing bunch of future warning)
matplotlib (newest version)
PIL (newest version)
skimage (newest version)
sklearn (newest version)
corelmltools 3.2
The data for Hiragana, Katakana, and Kanji can be downloaded from the ETL database website. The data for Kuzushiji can be found in the KMINST database. See Reference below.
read_{hira, kana, kanj}.py - save the whole data into one npz file
modify_{hira, kana, kanj}.py - create the train_images, train_labels, test_images, and test_labels files
{hiragana, katakana, kanji}_CNN.py - create a h5 model
convert_{hiragana, katakana, kanji}.py - convert h5 to mlmodel (See Additional Notes below to run this python file without any errors.)
| Model | Hiragana | Katakana | Kanji | Kuzushiji |
|---|---|---|---|---|
| Accuracy | 98.4% | 98.6% | 98.6% | 95.6% |
Note: these are the results I got after running the CNN models, results may vary slightly in every trial.
If tensorflow doesn't work at first, try running this command: /Applications/Python 3.7/Install Certificates.command
When converting h5 to mlmodel, downgrade keras 2.4.3 to keras 2.2.4.
To fix potential errors, run the following command:
tf_upgrade_v2 --infile /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py --outfile /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py
Also, go into the optimizers.py file within keras and manually change this in line 75:
original:allowed_kwargs = {'clipnorm', 'clipvalue'}
changed version:allowed_kwargs = {'clipnorm', 'clipvalue','name','learning_rate'}
- Electrotechnical Laboratory, Japanese Technical Committee for Optical Character Recognition, ETL Character Database, 1973-1984.
- Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, David Ha, "Deep Learning for Classical Japanese Literature", arXiv:1812.01718. KMNIST Dataset" (created by CODH), adapted from "Kuzushiji Dataset" (created by NIJL and others), doi:10.20676/00000341