afl's Introduction

Implementation of "Robust offline handwritten character recognition through exploring writer-independent features under the guidance of printed data" (AFL)

Paper

YapingZhang, Shan Liang, Shuai Nie, Wenju Liu, Shouye Peng, "Robust offline handwritten character recognition through exploring writer-independent features under the guidance of printed data", PR Letter 2018

Dependency

Please use python3, as we cannot guarantee its compatibility with python2.
Our code is based on Anaconda.
The version of Tensorflow we use is 1.10.1.
Other depencencies:
```
pip install keras
```

Usage

Clone the repo.

git clone https://github.com/AprilYapingZhang/AFL.git
cd AFL

Using ready-made data

Download the prepared data in hdf5 from Baidu Yun with passwd pf7f , to the repo root, and uncompress it.

NOTE: For the raw CASIA-HWDB, built by the CASIA, are released for academic research free of cost under an agreement.

Make sure the structure looks like the following:

data/:
CASIA_HWDB_1.0_1.1_data
data/CASIA_HWDB_1.0_1.1_data:
norm_hand_pair_3755.hdf5  trn-HWDB1.0-1.1-3756-uint8.hdf5  tst-HWDB1.0-1.1-3756-uint8.hdf5

Run model
- Download the pretrain weights from Baidu Yun with passwd ie3j , to the repo root.
- Run Baseline:
```
python baseline.py --data_dir ./data
```
- Run AFL model:
```
python main.py --data_dir ./data --pretrain_weights ./pre_weights.hdf5
```

Bibtex

@article{zhang2018robust,
  title={Robust offline handwritten character recognition through exploring writer-independent features under the guidance of printed data},
  author={Zhang, Yaping and Liang, Shan and Nie, Shuai and Liu, Wenju and Peng, Shouye},
  journal={Pattern Recognition Letters},
  volume={106},
  pages={20--26},
  year={2018},
  publisher={Elsevier}
}