This is the official Tensorflow implementation of the paper:

Yizhi Wang and Zhouhui Lian. Exploring Font-independent Features for Scene Text Recognition. ACM Multimedia. 2020.

As scene texts in novel font styles only make up a small proportion in existing benchmarks, we collect 100 text images with novel or unusual font styles to form a new dataset named as the Novel Font Scene Text (NFST) dataset (download link).

We compare our method with other two state-of-the-art methods (ASTER and SAR) whose codes are publicly available. Our method significantly outperforms others on this dataset (see the following table), whose robustness to font style variance is proved.

• python 3.6
• TensorFlow 1.15

Please use Anaconda to build the environment (otherwise, some errors may occur):

conda create -n efifstr python=3.6
source activate efifstr
pip install editdistance edit_distance
conda install tensorflow-gpu=1.15 pillow matplotlib

We reuse some code from ASTER, which is derived from Tensorflow Object Detection API.

1. Go to c_ops/ and run build.sh to build the custom operators
2. Execute protoc protos/*.proto --python_out=. to build the protobuf files

The pretrained model files are available on this link (Update: new link has better performance). Download pretrained-model.zip and extract it under experiments/demo/log/.

To test the preformance of the pretrained-model, simply execute:

python eval.py

This will output the recognition result of NFSF dataset.

To test on a single image (data/demo.jpg), simply execute:

python demo.py

Data preparation scripts for several popular scene text datasets are located under tools. See their source code for usage.

The multi-font glyph images are available on this link. Download glyphs-325-fonts.npy and put it under data/.

To run the example training, run

python train.py

Change the configuration in experiments/demo/trainval.prototxt to configure your own training process.

During the training, you can run a separate program to repeatedly evaluates the produced checkpoints.

python eval.py

Evaluation configuration is also in trainval.prototxt.