PIMNet: A Parallel, Iterative and Mimicking Network for Scene Text Recognition

Introduction

The implementation of the paper "PIMNet: A Parallel, Iterative and Mimicking Network for Scene Text Recognition", ACM MM 2021 (paper).

PIMNet is a parallel and iterative based text recognition methods, where the easy-first decoding strategy is adopted. Additionally, the mimicking learning is used to help the learning of the parallel decoder.

How to use

Runtime Environment

Python3.5
TensorFlow 1.12.0

Details can be found in requirements.txt

Train

• Please prepare the LMDB of Synth90K and SynthText (or download from the links shared by others), and offline-augmentented images provided by SRN.

Run

python3 train.py --checkpoints /path/to/save/checkpoints --train_data_dir /path/to/train/data/lmdb/1 /path/to/train/data/lmdb/2 /path/to/train/data/lmdb/3... --test_data_dir /path/to/validation/dataset -g "0" -b 256 -v 256

For more details about the hyper-parameters, please refer to config.py.

Evaluation

• Please prepare the LMDB of the benchmarks (or download from the links shared by others).

python3 test.py --checkpoints /path/to/the/checkpoints --test_data_dir /path/to/evaluation -g "0"

Pretrained Model

The model trained only on synthetic datasets with mimicking learning can be found at BaiduNetDisk (key: a787) or GoogleDrive.

Citation

If you find our method useful for your reserach, please cite

@inbook{10.1145/3474085.3475238,
author = {Qiao, Zhi and Zhou, Yu and Wei, Jin and Wang, Wei and Zhang, Yuan and Jiang, Ning and Wang, Hongbin and Wang, Weiping},
title = {PIMNet: A Parallel, Iterative and Mimicking Network for Scene Text Recognition},
year = {2021},
isbn = {9781450386517},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3474085.3475238},
booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
pages = {2046–2055},
numpages = {10}
}