AttentionOCR

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Scene text recognition

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- - SourceHanSans-Normal.ttf
- attention_maps_gt_1.jpg
- attention_maps_gt_8454.jpg
- attention_maps_gt_8459.jpg
- attention_maps_gt_8473.jpg
- attention_maps_gt_8601.jpg
- attention_maps_gt_8622.jpg
- attention_maps_gt_918.jpg
- attention_maps_gt_94.jpg
- loss.png
- viz.png
- web.png
- icdar_labels.txt
- __init__.py
- inception_v4.py
- model.py
- tensorpack_model.py
- Attention_Mechanism_in_Deep_Learning.pdf
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common.py
config.py
dataset.py
eval.py
export.py
parse_dict.py
README.md
test.py
text_dataflow.py
train.py

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## Introduction

This is the **ranked No.1** tensorflow based scene text spotting algorithm on [__ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text__](https://rrc.cvc.uab.es/?ch=14) (Latin Only, Latin and Chinese), futhermore, the algorithm is also adopted in [__ICDAR2019 Robust Reading Challenge on Large-scale Street View Text with Partial Labeling__](https://rrc.cvc.uab.es/?ch=16) and [__ICDAR2019 Robust Reading Challenge on Reading Chinese Text on Signboard__](https://rrc.cvc.uab.es/?ch=12).

Scene text detection algorithm is modified from [__Tensorpack FasterRCNN__](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN), and we only open source code in this repository for scene text recognition. I upload ICDAR2019 ArT competition model to docker hub, please refer to [Docker](#Docker). For more details, please refer to our [__arXiv technical report__](https://arxiv.org/abs/1912.04561).

Our text recognition algorithm not only recognizes Latin and Non-Latin characters, but also supports horizontal and vertical text recognition in one model. It is convenient for multi-lingual arbitrary-shaped text recognition.

**Note that the competition model in docker container as described in [__our technical report__](https://arxiv.org/abs/1912.04561) is slightly different from the recognition model trained from this updated repository.**

## Dependencies

```
python 3
tensorflow-gpu 1.14
tensorpack 0.9.8
pycocotools
```

## Usage

First download and extract multiple text datasets in base text dir, please refer to dataset.py for dataset preprocess and multiple datasets.

### Multiple Datasets

```
$(base_dir)/lsvt
$(base_dir)/art
$(base_dir)/rects
$(base_dir)/icdar2017rctw
```

You can also synthesize text recognition data for data augmentation, please refer to [__TextRecognitionDataGenerator__](https://github.com/Belval/TextRecognitionDataGenerator). It is helpful for long text recognition and attention-based language model because you can directly synthesize text images from NLP corpus. Then you should rewrite dataset.py for synthetic text dataset.

```
$(base_dir)/synthetic_text
```

### Train

First, download pretrained [__inception v4 checkpoint__](https://github.com/tensorflow/models/tree/master/research/slim) and put it in ./pretrain folder. 
Then you can modify your gpu lists in config.py for specified gpus and then run:
```
python train.py
```
You can visualize your training steps via tensorboard:
```
tensorboard --logdir='./checkpoint'
```
![](https://raw.githubusercontent.com/zhang0jhon/AttentionOCR/master/imgs/loss.png)

Use ICDAR2019-LSVT, ICDAR2019-ArT, ICDAR2019-ReCTS for default training, you can change it with your own training data.

### Evaluation

```
python eval.py --checkpoint_path=$(Your model path)
```

Use ICDAR2017RCTW for default evaluation with Normalized Edit Distance metric(1-N.E.D specifically), you can change it with your own evaluation data.

### Export

Export checkpoint to tensorflow pb model for inference.

```
python export.py --pb_path=$(Your tensorflow pb model save path) --checkpoint_path=$(Your trained model path)
```

### Test

Load tensorflow pb model for text recognition.
```
python test.py --pb_path=$(Your tensorflow pb model save path) --img_folder=$(Your test img folder)
```
Default use ICDAR2019-ArT for test, you can change it with your own test data.

## Visualization

Scene text detection and recognition result:

![](https://raw.githubusercontent.com/zhang0jhon/AttentionOCR/master/imgs/viz.png)

Scene text recognition attention maps:

![](https://raw.githubusercontent.com/zhang0jhon/AttentionOCR/master/imgs/attention_maps_gt_1.jpg)
![](https://raw.githubusercontent.com/zhang0jhon/AttentionOCR/master/imgs/attention_maps_gt_8454.jpg)

To learn more about attention mechanism, please refer to [Attention Mechanism in Deep Learning](reference/Attention_Mechanism_in_Deep_Learning.pdf).

## Docker

I upload ICDAR2019 scene text recognition model include text detection and recognition to [__Docker Hub__](https://hub.docker.com/repository/docker/zhang0jhon/demo).

After [__nvidia-docker__](https://github.com/NVIDIA/nvidia-docker) installed, run:

```
docker pull zhang0jhon/demo:ocr
docker run -it -p 5000:5000 --gpus all zhang0jhon/demo:ocr bash
cd /ocr/ocr
python flaskapp.py
```

Then you can test with your data via browser:

```
$(localhost or remote server ip address):5000
```
![](https://raw.githubusercontent.com/zhang0jhon/AttentionOCR/master/imgs/web.png)

AttentionOCR

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