Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition [AAAI-2019]

Introduction

This is an unofficial implementation of Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition
Official Torch implementation can be found here
Another PyTorch implementation can be found here

How to use

Install

pip3 install -r requirements.txt

Demo

• Download the pretrained model from BaiduYun and unzip it.
• Run

python3 test.py --test_data_dir ./demo_data --checkpoints ./sar_synall_lmdb_checkpoints_2epochs -g "0" --vis_dir ./visualize

• Results will be printed and attention weights visualizing images can be found in directory './visualize'

Train

• Data prepare
  LMDB format is suggested. refer here to generate data in LMDB format. Also raw images with annoation file (json or txt) is also supported. The stucture of annoation file please refer to txt or json
• Run
  LMDB:

   python3 train.py --checkpoints /path/to/save/checkpoints --train_data_dir /path/to/your/train/LMDB/data/dir --test_data_dir /path/to/your/test/LMDB/data/dir  -g "0"
  

  Raw images:

   python3 train.py --checkpoints /path/to/save/checkpoints --train_data_dir /path/to/your/train/images/dir --train_data_gt /path/to/your/train/annotation/file(txt or json) --test_data_dir /path/to/your/test/images/dir --test_data_gt /path/to/your/train/annotation/file(txt or json) -g "0"
  

  More hyper-parameters please refer to config.py

Test

Similar to demo and you can also provide annotation and it will calculate accuracy

python3 test.py --test_data_dir /path/to/your/test/images/dir --test_data_gt /path/to/your/test/annotation/file(optional) --checkpoints /path/to/trained/checkpoints -g "0"

Export frozen model

If you want to create a predicion server, you can export the frozen model with this command

python3 freeze.py

By default it will take the last checkpoint in the ./checkpoints folder. To change it, use the --checkpoints parameter.

Reproduced results

	IC13	IC15	SVTP	CUTE
Official	91.0	69.2	76.4	83.3
This One	91.8	69.6	75.1	83.6

Examples