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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Convolutional recurrent network in pytorch
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Convolutional Recurrent Neural Network
======================================
This software implements the Convolutional Recurrent Neural Network (CRNN) in pytorch.
Origin software could be found in [crnn](https://github.com/bgshih/crnn)
Run demo
--------
A demo program can be found in ``demo.py``. Before running the demo, download a pretrained model
from [Baidu Netdisk](https://pan.baidu.com/s/1pLbeCND) or [Dropbox](https://www.dropbox.com/s/dboqjk20qjkpta3/crnn.pth?dl=0).
This pretrained model is converted from auther offered one by ``tool``.
Put the downloaded model file ``crnn.pth`` into directory ``data/``. Then launch the demo by:
python demo.py
The demo reads an example image and recognizes its text content.
Example image:
Expected output:
loading pretrained model from ./data/crnn.pth
a-----v--a-i-l-a-bb-l-ee-- => available
Dependence
----------
* [warp_ctc_pytorch](https://github.com/SeanNaren/warp-ctc/tree/pytorch_bindings/pytorch_binding)
* lmdb
Train a new model
-----------------
1. Construct dataset following [origin guide](https://github.com/bgshih/crnn#train-a-new-model). If you want to train with variable length images (keep the origin ratio for example), please modify the `tool/create_dataset.py` and sort the image according to the text length.
2. Execute ``python train.py --adadelta --trainRoot {train_path} --valRoot {val_path} --cuda``. Explore ``train.py`` for details.
Cite
----
```tex
@article{shi2016end,
title={An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition},
author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
journal={IEEE transactions on pattern analysis and machine intelligence},
volume={39},
number={11},
pages={2298--2304},
year={2016},
publisher={IEEE}
}
```
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Xin Zhang · University of Waikato · New Zealand
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:85adf0f9828ac4b1, topic:neural-network