NRTR

NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition

Abstract

Scene text recognition has attracted a great many researches due to its importance to various applications. Existing methods mainly adopt recurrence or convolution based networks. Though have obtained good performance, these methods still suffer from two limitations: slow training speed due to the internal recurrence of RNNs, and high complexity due to stacked convolutional layers for long-term feature extraction. This paper, for the first time, proposes a no-recurrence sequence-to-sequence text recognizer, named NRTR, that dispenses with recurrences and convolutions entirely. NRTR follows the encoder-decoder paradigm, where the encoder uses stacked self-attention to extract image features, and the decoder applies stacked self-attention to recognize texts based on encoder output. NRTR relies solely on self-attention mechanism thus could be trained with more parallelization and less complexity. Considering scene image has large variation in text and background, we further design a modality-transform block to effectively transform 2D input images to 1D sequences, combined with the encoder to extract more discriminative features. NRTR achieves state-of-the-art or highly competitive performance on both regular and irregular benchmarks, while requires only a small fraction of training time compared to the best model from the literature (at least 8 times faster).

Dataset

Train Dataset

trainset	instance_num	repeat_num	source
SynthText	7266686	1	synth
Syn90k	8919273	1	synth

Test Dataset

testset	instance_num	type
IIIT5K	3000	regular
SVT	647	regular
IC13	1015	regular
IC15	2077	irregular
SVTP	645	irregular
CT80	288	irregular

Results and Models

Methods	Backbone		Regular Text			Irregular Text		download
		IIIT5K	SVT	IC13	IC15	SVTP	CT80
NRTR	R31-1/16-1/8	94.7	87.3	94.3	73.5	78.9	85.1	model \| log
NRTR	R31-1/8-1/4	95.2	90.0	94.0	74.1	79.4	88.2	model \| log

:::{note}

For backbone R31-1/16-1/8:
- The output consists of 92 classes, including 26 lowercase letters, 26 uppercase letters, 28 symbols, 10 digital numbers, 1 unknown token and 1 end-of-sequence token.
- The encoder-block number is 6.
- 1/16-1/8 means the height of feature from backbone is 1/16 of input image, where 1/8 for width.
For backbone R31-1/8-1/4:
- The output consists of 92 classes, including 26 lowercase letters, 26 uppercase letters, 28 symbols, 10 digital numbers, 1 unknown token and 1 end-of-sequence token.
- The encoder-block number is 6.
- 1/8-1/4 means the height of feature from backbone is 1/8 of input image, where 1/4 for width. :::

Citation

@inproceedings{sheng2019nrtr,
  title={NRTR: A no-recurrence sequence-to-sequence model for scene text recognition},
  author={Sheng, Fenfen and Chen, Zhineng and Xu, Bo},
  booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)},
  pages={781--786},
  year={2019},
  organization={IEEE}
}