A repo for machine-learning powered image classification of handwritten Chinese characters
Full release date: July 4, 2021
November 10, 2021 Update: Enabled writing the characters on mobile devices.
Novermber 30, 2021 Update: Implemented visitor map for website.
!pip install tensorflowjs
!tensorflowjs_converter --input_format keras "model.h5" "docs/model"TensorFlow, Numpy, Pandas, Matplotlib, CV2, OS
Found here
For this data 100 people wrote each of the 15 characters 10 times
suite_id is for each volunteer (100 total)
sample_id is for each sample of each volunteer (10 total) i.e. each volunteer writes each character 10 times
code is used to identify each character in their sequence i.e. code is the ith character in order
i.e. 零 is 1, 一 is 2, 二 is 3, ... 九 is 10
value is the numerical value of the character i.e. 5
character is the actual symbol i.e. 五
Optimizer: ADAM
Loss function: Sparse Categorical Cross Entropy
Epochs: 15
Loss: 0.0844
Accuracy: 0.9890 (98.90%)
Loss: 0.8035
Accuracy: 0.7753 (77.53%)
Note: The outputs of these models are given in the range from 0 to 14. In order to convert to characters, you can use this convenient list.
characters = ('零', '一', '二', '三', '四', '五', '六', '七', '八', '九', '十', '百', '千', '万', '亿')Prediction data:
In [1]: PredictionData[50]
Out [1]: array([3.6610535e-07, 1.8792988e-28, 2.2422669e-12, 1.7063133e-10,
2.6500999e-09, 5.2112355e-06, 3.2691182e-07, 8.3664847e-05,
3.6446830e-13, 9.9990106e-01, 1.1595256e-14, 5.7882625e-07,
9.6461701e-09, 3.3319463e-07, 8.4753528e-06], dtype=float32)
Prediction data:
In [2]: PredictionData[500]
Out [2]: array([3.83269071e-04, 4.26350415e-21, 5.74980230e-10, 5.73274974e-07,
4.58842493e-04, 8.47115181e-03, 6.79080131e-07, 1.44536525e-05,
8.73805250e-10, 1.13052677e-03, 2.78494355e-07, 9.84949350e-01,
2.79740023e-04, 4.30813758e-03, 2.93952530e-06], dtype=float32)
In your Python code, enter the following code to import the models.
import tensorflow as tf
model = tf.keras.models.load_model("model", compile=True)The outputs of these models are given in the range from 0 to 14. In order to convert to characters, you can use this convenient list.
characters = ('零', '一', '二', '三', '四', '五', '六', '七', '八', '九', '十', '百', '千', '万', '亿')You can then use this model in for other uses! Enjoy!
This should include the data folder, the chinese_mnist.csv csv file, and chinese_mnist.tfrecords.
For future reference let's call this directory folder1.
Change directory to the /folder1/data/data/ directory. On macOS, this might like something like /Users/user_name_here/Desktop/folder1/data/data/.