2D Attentional Irregular Scene Text Recognizer
November 29, 2019 ยท View on GitHub
Unofficial PyTorch implementation of the paper, which transforms the irregular text with 2D layout to character sequence directly via 2D attentional scheme. They utilize a relation attention module to capture the dependencies of feature maps and a parallel attention module to decode all characters in parallel.
At present, the accuracy of the paper cannot be achieved. And i borrowed code from deep-text-recognition-benchmark
model
result
Test on ICDAR2019 with only 51.15%, will continue to improve.
Feature
- Output image string once not like the seqtoseq model
Requirements
Pytorch >= 1.1.0
Test
-
download the pretrained model Baidu password: kdah.
-
test on images which in demo_image folder
python demo.py --image_folder demo_image --saved_model <model_path/best_accuracy.pth>
- some examples
| demo images | Bert_OCR |
|---|---|
 | available |
 | shakesshack |
 | london |
 | greenstead |
 | toast |
 | merry |
 | underground |
 | ronaldo |
 | bally |
 | university |
- result on benchmark data sets
| IIIT5k_3000 | SVT | IC03_860 | IC03_867 | IC13_857 | IC13_1015 | IC15_1811 | IC15_2077 | SVTP | CUTE80 |
|---|---|---|---|---|---|---|---|---|---|
| 84.367 | 79.907 | 91.860 | 91.465 | 88.448 | 86.010 | 65.654 | 63.215 | 68.527 | 81.185 |
total_accuracy: 78.423
Train
- I prepared a small dataset for train.The image and labels are in
./dataset/BAIDU.
python train.py --root ./dataset/BAIDU/images/ --train_csv ./dataset/BAIDU/small_train.txt --val_csv ./dataset/BAIDU/small_train.txt