Chinese Calligraphy Character Recognition with Intra-class Variant Clustering

The goal of this project is to recognise the 100 most commonly used chinese characters, written in calligraphy in the semi-cursive script. As compared to handwritten chinese characters, chinese calligraphy characters are harder to recognise, even for trained experts because they can be written in a less restrictive way to express the author’s personality and emotions... (read more here)

Sample data from character "总":

Requirements

lear-gist-python is used to extract GIST features. The installation instruction can be found in the repo.

The other depedencies can be installed by running:

$ pip install -r requirements.txt

Notes

• notebooks/* - ipynb files to collect and preprocess the data.
• train.ipynb - Demo to train the model. The data directories and model hyperparameters have to be specified in config.yaml.
• data/cccr/* - Train, validation and test set.
• data/common.txt - The most common chinese characters arranged in descending order, ie. the first 100 lines contain the 100 most commonly used characters.