CASIA OLHWCR-TF

Online handwriting Chinese character recognition using Tensorflow 2, Keras & Flask , based on CASIA's GB2312 level-1 dataset

SCREENSHOTS

INSTALLATION

• from within source directory locally

  pip install .

USAGE

✉️
The model restored from the pre-saved checkpoint (located in 'olccr/recognition/conf/checkpoint/weights.hdf5') is not fully trained, and its hyperparameters are not tuned. The app above is just for demonstrating the idea. Further training and experimentation should be done.

Running recognition App

From the Bash/CMD shell execute the following command, then visit http://127.0.0.1:5000/:

(ENV)$ olccr

Re-training model

• Preparing raw data

Run the following command to unzip and patch the raw data:

(ENV)$ olccr_prepare

• Making dataset

To generate the training and validation dataset, simply run:

(ENV)$ olccr_preprocess -t -v

• Training model

Run or re-run after interruption the following command to train or resume training the network, respectively

(ENV)$ olccr_train -V 1

Installing trained weights

With default setup, for example, the checkpointed weights should be saved as olccr/data/ckpts/weights.hdf5. To apply the latest weights, copy it to the App's recognition configuration directory olccr/recognition/conf/checkpoint/ replacing the same name file, then restart the App.

REFERENCES

• https://github.com/taosir/cnn_handwritten_chinese_recognition
• https://github.com/Leimi/drawingboard.js
• https://github.com/michael-zhu-sh/CASIA/blob/master/OLHWDB/OLHWDB1.cpp
• https://zhuanlan.zhihu.com/p/101513445
• http://www.nlpr.ia.ac.cn/databases/handwriting/Online_database.html
• http://www.herongyang.com/GB2312/GB2312-to-Unicode-Map-Level-1-Characters.html