# Attention_ocr
**Repository Path**: advancer-debug/Attention_ocr
## Basic Information
- **Project Name**: Attention_ocr
- **Description**: Encoder and decoder with attention model for chinese_ocr in Pytorch 1.0
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-07-03
- **Last Updated**: 2020-12-31
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
 
# Attention OCR in Pytorch
This repository implements the the encoder and decoder model with attention model for OCR in pytorch, and its arithmetic is based on the paper <[Attention-based Extraction of Structured Information from Street View Imagery](https://arxiv.org/pdf/1704.03549.pdf)>.
⛳️ **Network Framework:**
## Prerequsites
- Pytorch 1.0+ ✔️
- python opencv 4.0.0+ ✔️
- scikit-image 0.15.0 ✔️
## Usage
### Train
#### Dataset
We need a file (specified by parameter **TRAIN_DIR**, **TEST_DIR**) containing the path of images and the corresponding characters.
1. Download the data or you can use the Small_Synthetic_Chinese_String_Dataset, every image fixed 10 characters. 344000 training image, and about 20000 testing image, please download it via [BaiDu](https://pan.baidu.com/s/1hIurFJ73XbzL-QG4V-oe0w#list/path=%2F)
2. The train.txt and test.txt are created as the follow form:
```
# path to image and label
./images/33069953_4129036931.jpg 到此刻，不要煮的时间
```
#### Start train
Trian the model with the bleow command:
```bash
python train.py --TRAIN_DIR ./data/train.txt --TEST_DIR ./data/test.txt --batch_size 64 --input_h 32 --input_w 100 --lr 0.001 --use_gpu True
```
After a while, you will see something like the following output in out.txt:
```
...
2019-05-25 17:09:59,176: Epoch: 22 Batch: 4000 loss=0.016591 seq_acc=0.956629 char_acc=0.995143
2019-05-25 17:10:10,447: Epoch: 22 Batch: 4100 loss=0.016654 seq_acc=0.956391 char_acc=0.995114
2019-05-25 17:10:21,555: Epoch: 22 Batch: 4200 loss=0.016669 seq_acc=0.956269 char_acc=0.995099
2019-05-25 17:10:32,426: Epoch: 22 Batch: 4300 loss=0.016837 seq_acc=0.955952 char_acc=0.995051
2019-05-25 17:10:43,875: Epoch: 22 Batch: 4400 loss=0.016873 seq_acc=0.955824 char_acc=0.995038
2019-05-25 17:10:54,621: 尺寸更高清晰度更好材 ===> gt: 尺寸更高清晰度更好材
2019-05-25 17:10:54,622: Epoch: 22 Batch: 4500 loss=0.016806 seq_acc=0.955910 char_acc=0.995049
2019-05-25 17:11:05,843: Epoch: 22 Batch: 4600 loss=0.016755 seq_acc=0.955931 char_acc=0.995053
2019-05-25 17:11:16,932: Epoch: 22 Batch: 4700 loss=0.016758 seq_acc=0.955924 char_acc=0.995050
2019-05-25 17:11:28,228: Epoch: 22 Batch: 4800 loss=0.016769 seq_acc=0.955951 char_acc=0.995055
2019-05-25 17:11:39,340: Epoch: 22 Batch: 4900 loss=0.016807 seq_acc=0.955762 char_acc=0.995034
2019-05-25 17:11:50,375: 会让上司对你的印象大 ===> gt: 会让上司对你的印象大
2019-05-25 17:11:50,375: Epoch: 22 Batch: 5000 loss=0.016792 seq_acc=0.955759 char_acc=0.995036
2019-05-25 17:12:01,674: Epoch: 22 Batch: 5100 loss=0.016750 seq_acc=0.955778 char_acc=0.995041
2019-05-25 17:12:12,553: Epoch: 22 Batch: 5200 loss=0.016693 seq_acc=0.955877 char_acc=0.995052
2019-05-25 17:12:23,674: Epoch: 22 Batch: 5300 loss=0.016615 seq_acc=0.956002 char_acc=0.995070
2019-05-25 17:12:32,679: Epoch: 22 training: loss=0.016603 epoch_seq_acc=0.955927 epoch_char_acc=0.995063
```
✏️ Note that it takes quite a long time to reach convergence, since we are training the RCNN and attention model simultaneously.
### Test
The pretrianed model could download via [Baidu](https://pan.baidu.com/s/1QOnMnZV-d66AaxELz1EMCw), password( ++wxx8++ ), and copy it into `./checkpoints`,and run:
```
python demo.py --img ./images/* --index_to_char ./data/index_to_char.json --checkpoints ./checkpoints/model_best.pt --use_gpu True
```
**Input Images:**
**Output Text**
```
====== Start Ocr ======
Path: image/1.jpg ===>>> 正如只有经过泥泞的道 ===>>> time cost: 0.097589
Path: image/2.jpg ===>>> 成功更容易光顾磨难和 ===>>> time cost: 0.019335
Path: image/3.jpg ===>>> 提供实时翻译复制及分 ===>>> time cost: 0.030660
Path: image/4.jpg ===>>> 小或者是yolo算法 ===>>> time cost: 0.023226
Path: image/5.jpg ===>>> 下一步就是设计算法得 ===>>> time cost: 0.017680
```
## Reference
1. [crnn.pytorch](https://github.com/meijieru/crnn.pytorch)
1. [Attention_ocr.pytorch](https://github.com/chenjun2hao/Attention_ocr.pytorch)
1. [Attention-OCR](https://github.com/da03/Attention-OCR)
## TODO
- [ ] Training model with the string have different lengths.👊
- [ ] Training model with different basebone, such as resnet50.👊