mingming008/transformer_str

PyTorch implementation of my new method for Scene Text Recognition (STR) based on Transformer,Equipped with Transformer, this method outperforms the best model of the aforementioned deep-text-recognition-benchmark by 7.6% on CUTE80.

Transformer-based Scene Text Recognition (Transformer-STR)

• PyTorch implementation of my new method for Scene Text Recognition (STR) based on Transformer.

I adapted the four-stage STR framework devised by deep-text-recognition-benchmark, and replaced the Pred. stage with Transformer.

Equipped with Transformer, this method outperforms the best model of the aforementioned deep-text-recognition-benchmark by 7.6% on CUTE80.

Download pretrained weights from here

This pre-trained weights trained on Synthetic dataset for about 700K iters.

Git clone this repo and download the weights file, move it to checkpoints directory.

Download lmdb dataset for traininig and evaluation from here(provided by deep-text-recognition-benchmark)

data_lmdb_release.zip contains below.
training datasets : MJSynth (MJ)[1] and SynthText (ST)[2]
validation datasets : the union of the training sets IC13[3], IC15[4], IIIT[5], and SVT[6].
evaluation datasets : benchmark evaluation datasets, consist of IIIT[5], SVT[6], IC03[7], IC13[3], IC15[4], SVTP[8], and CUTE[9].

Training

Please configure your data_dir in config.py file, then run:

python tools/train.py

Evaluation on CUTE80

The Transformer-base STR achieves 0.815972 accuracy on CUTE80, outperforming the best model of deep-text-recognition-benchmark, which is 0.74

If you want to reproduce the evaluation result, please run:

python evaluation.py

Make sure your cute80_dir and saved_model path is correct. you'll get the result 0.815972

Contact

Feel free to contact me ([email protected]).

License

This project is released under the Apache 2.0 license.

References

deep-text-recognition-benchmark