SAR

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

Abstract

Recognizing irregular text in natural scene images is challenging due to the large variance in text appearance, such as curvature, orientation and distortion. Most existing approaches rely heavily on sophisticated model designs and/or extra fine-grained annotations, which, to some extent, increase the difficulty in algorithm implementation and data collection. In this work, we propose an easy-to-implement strong baseline for irregular scene text recognition, using off-the-shelf neural network components and only word-level annotations. It is composed of a 31-layer ResNet, an LSTM-based encoder-decoder framework and a 2-dimensional attention module. Despite its simplicity, the proposed method is robust and achieves state-of-the-art performance on both regular and irregular scene text recognition benchmarks.

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


-   `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]](#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](https://download.openmmlab.com/mmocr/textrecog/sar/sar_r31_academic.py) 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.

Citation

@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}
}