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Convolutional Recurrent Neural Network (CRNN) for image-based sequence recognition using Pytorch
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# CRNN Pytorch
## Quick Demo
```command
$ pip install -r requirements.txt
$ python src/predict.py -h
```
Everything is okay. Let's predict the demo images.
```command
$ 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
```
## CRNN + CTC
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."](http://arxiv.org/abs/1507.05717).
Blog article with more info: [https://ycc.idv.tw/crnn-ctc.html](https://ycc.idv.tw/crnn-ctc.html)
## Download Synth90k dataset
```command
$ 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",
}
```
## Pretrained Model
We pretrained the RCNN model on [Synth90k](http://www.robots.ox.ac.uk/~vgg/data/text/) dataset. The weights saved at `checkpoints/crnn_synth90k.pt`.
### Evaluate the model on the Synth90k dataset
```command
$ python src/evaluate.py
```
Evaluate 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 |
## Train your model
You could adjust hyper-parameters in `./src/config.py`.
And train crnn models,
```command
$ python src/train.py
```
## Acknowledgement
Please cite this repo. [crnn-pytorch](https://github.com/GitYCC/crnn-pytorch) if you use it.
Excerpt of 2,763 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:30875a4553c86cf2, topic:pytorch