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README.md

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+# MORAN: A Multi-Object Rectified Attention Network for Scene Text Recognition
+
+![](https://img.shields.io/badge/version-v2-orange.svg)
+
+MORAN is a network with rectification mechanism for general scene text recognition. The paper in [arXiv]() version is available now. 
+
+![](demo/MORAN_v2.gif)
+
+## Improvements of MORAN v2:
+
+- More stable rectification network for one-stage training
+- Replace VGG backbone by ResNet
+- Use bidirectional decoder (a trick borrowed from [ASTER](https://github.com/bgshih/aster))
+
+| <center>Dataset</center> | <center>IIIT5K</center> | <center>SVT</center> | <center>IC03</center> | <center>IC13</center> | <center>SVT-P</center> | <center>CUTE80</center> | <center>IC15 (1811)</center> | <center>IC15 (2077)</center> |
+| :---: | :---: | :---: | :---:| :---:| :---:| :---:| :---:| :---:|
+| MORAN v1 (two-stage training) | <center>91.2</center> | <center>**88.3**</center> | <center>**95.0**</center> | <center>92.4</center> | <center>76.1</center> | <center>77.4</center> | <center>74.7</center> | <center>68.8</center> |
+| <center>MORAN v2 (one-stage training)</center> | <center>**93.4**</center> | <center>**88.3**</center> | <center>94.2</center> | <center>**93.2**</center> | <center>**79.7**</center> | <center>**81.9**</center> | <center>**77.8**</center> | <center>**73.9**</center> |
+
+## Requirements
+
+- [PyTorch](https://pytorch.org/) 0.3.*
+- [TorchVision](https://pypi.org/project/torchvision/)
+- [Python](https://www.python.org/) 2.7.*
+- [OpenCV](https://opencv.org/) 2.4.*
+
+Use [pip](https://pypi.org/project/pip/) to install the following libraries.
+
+```bash
+    pip install -r requirements.txt
+```
+
+- [Colour](https://pypi.org/project/colour/)
+- [LMDB](https://pypi.org/project/lmdb/)
+- [matplotlib](https://pypi.org/project/matplotlib/)
+
+## Data Preparation
+Please convert your own dataset to lmdb format by using the [tool](https://github.com/bgshih/crnn/blob/master/tool/create_dataset.py) provided by [@Baoguang Shi](https://github.com/bgshih). You can also download the training and testing datasets prepared by us. The raw pictures of testing datasets can be found [here](https://github.com/chengzhanzhan/STR).
+
+- [about 20G training datasets and testing datasets](https://pan.baidu.com/s/1TqZfvoEhyv57yf4YBjSzFg), password: l8em
+
+## Training and Testing
+
+Modify the path to dataset folder in `train_MORAN.sh`:
+
+```bash
+	--train_nips path_to_dataset \
+	--train_cvpr path_to_dataset \
+	--valroot path_to_dataset \
+```
+
+And start training: (manually decrease the learning rate for your task)
+
+```bash
+	sh train_MORAN.sh
+```
+
+## Demo
+
+Download the model parameter file from the link above and put the `demo.pth` into root folder. Then, execute the `demo.py` for more visualizations.
+
+```bash
+	python demo.py
+``` 
+
+## Citation
+
+```
+@article{cluo2019moran,
+  author  = {Canjie Luo, Lianwen Jin, Zenghui Sun},
+  title   = {MORAN: A Multi-Object Rectified Attention network for Scene Text Recognition},
+  journal = {Pattern Recognition}, 
+  volume  = {}, 
+  number  = {}, 
+  pages   = {},
+  year    = {2019}, 
+}
+```
+
+## Acknowledgment
+The repo is developed based on [@Jieru Mei's](https://github.com/meijieru) [crnn.pytorch](https://github.com/meijieru/crnn.pytorch) and [@marvis'](https://github.com/marvis) [ocr_attention](https://github.com/marvis/ocr_attention). Thanks for your contribution.

demo.py

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+import torch
+from torch.autograd import Variable
+import tools.utils as utils
+import tools.dataset as dataset
+from PIL import Image
+from collections import OrderedDict
+import cv2
+from models.moran import MORAN
+
+model_path = './demo.pth'
+img_path = './demo/0.png'
+alphabet = '0:1:2:3:4:5:6:7:8:9:a:b:c:d:e:f:g:h:i:j:k:l:m:n:o:p:q:r:s:t:u:v:w:x:y:z:$'
+
+MORAN = MORAN(1, len(alphabet.split(':')), 256, 32, 100, BidirDecoder=True)
+
+if torch.cuda.is_available():
+    MORAN = MORAN.cuda()
+
+print('loading pretrained model from %s' % model_path)
+state_dict = torch.load(model_path)
+MORAN_state_dict_rename = OrderedDict()
+for k, v in state_dict.items():
+    name = k.replace("module.", "") # remove `module.`
+    MORAN_state_dict_rename[name] = v
+MORAN.load_state_dict(MORAN_state_dict_rename)
+
+for p in MORAN.parameters():
+    p.requires_grad = False
+MORAN.eval()
+
+converter = utils.strLabelConverterForAttention(alphabet, ':')
+transformer = dataset.resizeNormalize((100, 32))
+image = Image.open(img_path).convert('L')
+image = transformer(image)
+
+if torch.cuda.is_available():
+    image = image.cuda()
+image = image.view(1, *image.size())
+image = Variable(image)
+text = torch.LongTensor(1 * 5)
+length = torch.IntTensor(1)
+text = Variable(text)
+length = Variable(length)
+
+max_iter = 20
+t, l = converter.encode('0'*max_iter)
+utils.loadData(text, t)
+utils.loadData(length, l)
+output = MORAN(image, length, text, text, test=True, debug=True)
+
+preds, preds_reverse = output[0]
+demo = output[1]
+
+_, preds = preds.max(1)
+_, preds_reverse = preds_reverse.max(1)
+
+sim_preds = converter.decode(preds.data, length.data)
+sim_preds = sim_preds.strip().split('$')[0]
+sim_preds_reverse = converter.decode(preds_reverse.data, length.data)
+sim_preds_reverse = sim_preds_reverse.strip().split('$')[0]
+
+print('\nResult:\n' + 'Left to Right: ' + sim_preds + '\nRight to Left: ' + sim_preds_reverse + '\n\n')
+
+cv2.imshow("demo", demo)
+cv2.waitKey()