$ pip install -r requirements.txt
$ python src/predict.py -hEverything is okay. Let's predict the demo images.
$ python src/predict.py demo/*.jpg
device: cpu
Predict: 100% [00:00<00:00, 4.89it/s]
===== result =====
demo/170_READING_62745.jpg > reading
demo/178_Showtime_70541.jpg > showtime
demo/78_Novel_52433.jpg > novel
This is a Pytorch implementation of a Deep Neural Network for scene text recognition. It is based on the paper "An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition (2016), Baoguang Shi et al.".
Blog article with more info: https://ycc.idv.tw/crnn-ctc.html
$ cd data
$ bash download_synth90k.sh@InProceedings{Jaderberg14c,
author = "Max Jaderberg and Karen Simonyan and Andrea Vedaldi and Andrew Zisserman",
title = "Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition",
booktitle = "Workshop on Deep Learning, NIPS",
year = "2014",
}
@Article{Jaderberg16,
author = "Max Jaderberg and Karen Simonyan and Andrea Vedaldi and Andrew Zisserman",
title = "Reading Text in the Wild with Convolutional Neural Networks",
journal = "International Journal of Computer Vision",
number = "1",
volume = "116",
pages = "1--20",
month = "jan",
year = "2016",
}
We pretrained the RCNN model on Synth90k dataset. The weights saved at checkpoints/crnn_synth90k.pt.
$ python src/evaluate.pyEvaluate on 891927 Synth90k test images:
- Test Loss: 0.53042
| Decoded Method | Sequence Accuracy | Prediction Time |
|---|---|---|
| greedy | 0.93873 | 0.44398 ms/image |
| beam_search (beam_size=10) | 0.93892 | 6.9120 ms/image |
| prefix_beam_search (beam_size=10) | 0.93900 | 42.598 ms/image |
You could adjust hyper-parameters in ./src/config.py.
And train crnn models,
$ python src/train.pyPlease cite this repo. crnn-pytorch if you use it.
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