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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>.

⛳️ Network Framework:

image

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
  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:

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, 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
  2. Attention_ocr.pytorch
  3. Attention-OCR

TODO

  • Training model with the string have different lengths.👊
  • Training model with different basebone, such as resnet50.👊