sar_tf's Introduction

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"