mmocr/configs/textrecog/sar

Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition

Introduction

[ALGORITHM]

@inproceedings{li2019show,
  title={Show, attend and read: A simple and strong baseline for irregular text recognition},
  author={Li, Hui and Wang, Peng and Shen, Chunhua and Zhang, Guyu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  number={01},
  pages={8610--8617},
  year={2019}
}

Dataset

Train Dataset

trainset	instance_num	repeat_num	source
icdar_2011	3567	20	real
icdar_2013	848	20	real
icdar2015	4468	20	real
coco_text	42142	20	real
IIIT5K	2000	20	real
SynthText	2400000	1	synth
SynthAdd	1216889	1	synth, 1.6m in [1]
Syn90k	2400000	1	synth

Test Dataset

testset	instance_num	type
IIIT5K	3000	regular
SVT	647	regular
IC13	1015	regular
IC15	2077	irregular
SVTP	645	irregular, 639 in [1]
CT80	288	irregular

Results and Models

Methods	Backbone	Decoder		Regular Text				Irregular Text		download
			IIIT5K	SVT	IC13		IC15	SVTP	CT80
SAR	R31-1/8-1/4	ParallelSARDecoder	95.0	89.6	93.7		79.0	82.2	88.9	model | log
SAR	R31-1/8-1/4	SequentialSARDecoder	95.2	88.7	92.4		78.2	81.9	89.6	model | log

Chinese Dataset

Results and Models

Methods	Backbone	Decoder		download
SAR	R31-1/8-1/4	ParallelSARDecoder		model | log | dict

Notes:

• R31-1/8-1/4 means the height of feature from backbone is 1/8 of input image, where 1/4 for width.
• We did not use beam search during decoding.
• We implemented two kinds of decoder. Namely, ParallelSARDecoder and SequentialSARDecoder.
  • ParallelSARDecoder: Parallel decoding during training with LSTM layer. It would be faster.
  • SequentialSARDecoder: Sequential Decoding during training with LSTMCell. It would be easier to understand.
• For train dataset.
  • We did not construct distinct data groups (20 groups in [1]) to train the model group-by-group since it would render model training too complicated.
  • Instead, we randomly selected 2.4m patches from Syn90k, 2.4m from SynthText and 1.2m from SynthAdd, and grouped all data together. See config for details.
• We used 48 GPUs with total_batch_size = 64 * 48 in the experiment above to speedup training, while keeping the initial lr = 1e-3 unchanged.

References

[1] Li, Hui and Wang, Peng and Shen, Chunhua and Zhang, Guyu. Show, attend and read: A simple and strong baseline for irregular text recognition. In AAAI 2019.