An implementation of CVPR 2020 paper "SCATTER: Selective Context Attentional Scene Text Recognizer"

Paper | Pretrained model

Introduction

This is an unofficial implementation of paper "SCATTER: Selective Context Attentional Scene Text Recognizer" published at CVPR 2020.

Getting Started

Dependency

• This work was tested with PyTorch 1.6.0, CUDA 10.2, python 3.6.10 and Ubuntu 18.04.

conda install pytorch torchvision cudatoolkit=10.2 -c pytorch

• requirements : lmdb, pillow, nltk, natsort

pip3 install lmdb pillow nltk natsort

Dataset

• training dataset: MJSynth (MJ)[1], SynthText (ST)[2] and SynthAdd (SA) [3]
• validation datasets : the union of the training sets IC13[4], IC15[5], IIIT[6], and SVT[7].
  evaluation datasets : benchmark evaluation datasets, consist of IIIT[5], SVT[7], IC03[8], IC13[4], IC15[5], SVTP[9], and CUTE[10].

Pretrained Model

Two pretrained models are provided (Will be updated when better models are trained):

1. non-senstive: includes ten digits (0-9) and 26 characters (a-z).
2. sensitive: includes all readable characters.

Pretrained models can be downloaded here

Run demo

• With non-sensitve model

python demo.py --saved_model scatter-case-non-sensitive.pth --sensitive --image_folder <path_to_image_folder>

• With sensitve model

python demo.py --saved_model scatter-case-sensitive.pth --image_folder <path_to_image_folder>

Training and evaluation

Download lmdb dataset for traininig and evaluation provided by deep-text-recognition-benchmark from here

Download addition dataset SynthText_Add (SA) for training from here (includes raw images and lmdb format).

Training

python3 train.py --train_data data_lmdb_release/training --valid_data data_lmdb_release/validation --select_data MJ-ST-SA --batch_ratio 0.4-0.4-0.2 --sensitive 

Testing

python3 test.py --eval_data data_lmdb_release/evaluation --saved_model scatter-case-sensitive.pth --sensitive --data_filtering_off

Reported results

• Using evaluation set here

• Compare with result in the original paper and baseline model.

Model	IIIT5K	SVT	IC03	IC13	Regular Text	IC15	SVTP	CUTE	Irregular Text
Paper (non-sensitive)	93.7	92.7	96.3	93.9	94.0	82.2	86.9	87.5	83.7
Baseline	87.9	87.5	94.9	92.3	89.8	71.8	79.2	74.0	73.6
Our (sensitive)	93.5	90.9	95.0	93.6	93.4	78.6	83.4	83.3	80.0
Our (non-sensitive)	93.8	90.9	95.3	93.8	93.7	79.7	85.0	86.1	81.5

Acknowledgements

This code is built upon deep-text-recognition-benchmark.

Reference

[1] M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman. Synthetic data and artificial neural networks for natural scenetext recognition. In Workshop on Deep Learning, NIPS, 2014.
[2] A. Gupta, A. Vedaldi, and A. Zisserman. Synthetic data fortext localisation in natural images. In CVPR, 2016.
[3] Hui Li, Peng Wang, Chunhua Shen, Guyu Zhang. Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition. In AAAI, 2019
[4] D. Karatzas, F. Shafait, S. Uchida, M. Iwamura, L. G. i Big-orda, S. R. Mestre, J. Mas, D. F. Mota, J. A. Almazan, andL. P. De Las Heras. ICDAR 2013 robust reading competition. In ICDAR, pages 1484–1493, 2013.
[5] D. Karatzas, L. Gomez-Bigorda, A. Nicolaou, S. Ghosh, A. Bagdanov, M. Iwamura, J. Matas, L. Neumann, V. R.Chandrasekhar, S. Lu, et al. ICDAR 2015 competition on ro-bust reading. In ICDAR, pages 1156–1160, 2015.
[6] A. Mishra, K. Alahari, and C. Jawahar. Scene text recognition using higher order language priors. In BMVC, 2012.
[7] K. Wang, B. Babenko, and S. Belongie. End-to-end scenetext recognition. In ICCV, pages 1457–1464, 2011.
[8] S. M. Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong, andR. Young. ICDAR 2003 robust reading competitions. In ICDAR, pages 682–687, 2003.
[9] T. Q. Phan, P. Shivakumara, S. Tian, and C. L. Tan. Recognizing text with perspective distortion in natural scenes. In ICCV, pages 569–576, 2013.
[10] A. Risnumawan, P. Shivakumara, C. S. Chan, and C. L. Tan. A robust arbitrary text detection system for natural scene images. In ESWA, volume 41, pages 8027–8048, 2014.
[11] B. Shi, X. Bai, and C. Yao. An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. In TPAMI, volume 39, pages2298–2304. 2017.

Citation

Please consider citing this work in your publications if it helps your research.

@inproceedings{litman2020scatter,
  title={SCATTER: selective context attentional scene text recognizer},
  author={Litman, Ron and Anschel, Oron and Tsiper, Shahar and Litman, Roee and Mazor, Shai and Manmatha, R},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11962--11972},
  year={2020}
}