Fintech-Final HCCR

• Handwritten Chinese Character Recognition
• T‑Brain E.Sun Bank Artificial Intelligence Challenge 2021 Summer
• This repo is revised from Offline Handwritten Chinese Character Classifier.

Summary

Goal

Given the handwritten image below, recognize its corresponding Chinese character 巧.

It is a 801-class task. There are 800 kinds of characters in total to be recognized, and one for isnull.

Results

• Accuracy: 0.9054
• Top 7% (31/468)

Notes

• Data Augmentation
  • Shift, Zoom, Rotation
  • Combine other data source
• Error Analysis
  • Find out 78 isnull characters that model did not do well and enhance their training data
  • Distinguish 879 (801+78) classes first, and then filter out those 78 classes characters into isnull
  • Raise up to 2% in valid set.

Commands

Train

time python melnyk_net.py --log_dir models_1 --training_src combine

Continue Training

time python melnyk_net.py --log_dir models_1 --training_src combine --continue_training Trained_Model_Path

Inference Single Image

python predictor.py --img_path images/巧_YMmNG5.jpg --model_path model_combine.hdf5

Model Performance

Model File	Best Valid Accuracy	#Classes
model_combine.hdf5	0.9058	801
models_combine_similar.hdf5	0.9241	879

Hierachy

├───src
│   ├───melnyk_net.py
│   ├───predictor.py
│   └─── ....
├───training_data
│   ├───combine (training_src)
│   └───combine_similar (training_src)
└───models
    ├───models_1 (log_dir)
    │   ├───models_1_1.hdf5
    │   ├───models_1_2.hdf5
    │   └─── ....
    └─── ....

Contact

If you have any question, please feel free to contact me by sending email to r08946014@ntu.edu.tw