An Efficient Text Detection Model for Street Signs
Source abstract
Abstract
Text detection in natural scenes is a current research hotspot. The Efficient and Accurate Scene Text (EAST) detector model has fast detection speed and good performance but is ineffective in detecting long text regions owing to its small receptive field. In this study, we built upon the EAST model by improving the bounding box’s shrinking algorithm to make the model more accurate in predicting short edges of text regions; altering the loss function from balanced cross-entropy to Focal loss; improving the model’s learning ability on hard, positive examples; and adding a feature enhancement module (FEM) to increase the receptive field of the EAST model and enhance its detection ability for long text regions. The improved EAST model achieved better detection results on both the ICDAR2015 dataset and the Street Sign Text Detection (SSTD) dataset proposed in this paper. The precision and F1 scores of the model also demonstrated advantages over other models on the ICDAR2015 dataset. A comparison of the text detection effects between the improved EAST model and the EAST model showed that the proposed FEM was more effective in increasing the EAST detector’s receptive field, which indicates that it can improve the detection of long text regions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Handwritten Text Recognition Techniques · Vehicle License Plate Recognition · Hand Gesture Recognition Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528400, China
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
School of Information Engineering, Changchun Sci-Tech University, Changchun 130600, China
Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan
School of Electron and Information Engineering, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528400, China
Funding
National Social Science Fund of China
References (34)
Show source-provided references
1. Mahajan, S., and Rani, R. (2021). Text detection and localization in scene images: A broad review. Artif. Intell. Rev.10.1007/s10462-021-10000-8 ↗
2. Karatzas, D., Gomez-Bigorda, L., Nicolaou, A., Ghosh, S., Bagdanov, A., Iwamura, M., Matas, J., Neumann, L., Chandrasekhar, V.R., and Lu, S. (2015, January 23–26). ICDAR 2015 competition on Robust Reading. Proceedings of the 2015 13th International Conference on Document Analysis and Recognition (ICDAR), Tunis, Tunisia.10.1109/ICDAR.2015.7333942 ↗
3. Epshtein, B., Ofek, E., and Wexler, Y. (2010, January 13–18). Detecting text in natural scenes with stroke width transform. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.10.1109/CVPR.2010.5540041 ↗
4. Robust wide-baseline stereo from maximally stable extremal regions10.1016/j.imavis.2004.02.006 ↗
5. Lee, J., Lee, P., Lee, S., Yuille, A., and Koch, C. (2011, January 18–21). AdaBoost for Text Detection in Natural Scene. Proceedings of the 2011 International Conference on Document Analysis and Recognition (ICDAR), Beijing, China.10.1109/ICDAR.2011.93 ↗
6. Wang, K., and Belongie, S.J. (2010). Word spotting in the wild. Proceedings of the European Conference on Computer Vision, Springer.10.1007/978-3-642-15549-9_43 ↗
7. Tian, S., Pan, Y., Huang, C., Lu, S., Yu, K., and Tan, C.L. (2015, January 7–13). Text flow: A unified text detection system in natural scene images. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.10.1109/ICCV.2015.528 ↗
8. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
9. Faster R-CNN: Towards real-time object detection with region proposal networks10.1109/TPAMI.2016.2577031 ↗
10. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). SSD: Single shot MultiBox detector. Proceedings of the European Conference on Computer Cision, Springer International Publishing.10.1007/978-3-319-46448-0_2 ↗
11. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You only look once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
12. Liao, M., Shi, B., Bai, X., Wang, X., and Liu, W. (2017). TextBoxes: A Fast Text Detector with a Single Deep Neural Network. AAAI, 31, Available online: https://ojs.aaai.org/index.php/AAAI/article/view/11196.10.1609/aaai.v31i1.11196 ↗
13. TextBoxes++: A single-shot oriented scene text detector10.1109/TIP.2018.2825107 ↗
14. Tian, Z., Huang, W., He, T., He, P., and Qiao, Y. (2016). Detecting text in natural image with connectionist text proposal network. Proceedings of the European Conf. on Computer Vision, Springer.10.1007/978-3-319-46484-8_4 ↗
15. Shi, B., Bai, X., and Belongie, S. (2017, January 21–26). Detecting oriented text in natural images by linking segments. Proceedings of the IEEE Conference on Computer Vision and Pattern, Honolulu, HI, USA.10.1109/CVPR.2017.371 ↗
16. Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., and Liang, J. (2017, January 21–26). EAST: An efficient and accurate scene text detector. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.283 ↗
17. He, W., Zhang, X., Yin, F., and Liu, C. (2017, January 22–29). Deep direct regression for multi-oriented scene text detection. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.87 ↗
18. Song, Y., Cui, Y., Han, H., Shan, S., and Chen, X. (2018, January 20–24). Scene text detection via deep semantic feature fusion and attention-based refinement. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.10.1109/ICPR.2018.8546050 ↗
19. TextField: Learning a deep direction field for irregular scene text detection10.1109/TIP.2019.2900589 ↗
20. Fully convolutional networks for semantic segmentation10.1109/TPAMI.2016.2572683 ↗
21. Lin, T., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature pyramid networks for object detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
22. Hu, H., Zhang, C., Luo, Y., Wang, Y., Han, J., and Ding, E. (2017, January 22–29). WordSup: Exploiting Word Annotations for Character based Text Detection. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.529 ↗
23. Cao, D., Dang, J., and Zhong, Y. (2021). Towards Accurate Scene Text Detection with Bidirectional Feature Pyramid Network. Symmetry, 13.10.3390/sym13030486 ↗
24. ReLaText: Exploiting visual relationships for arbitrary-shaped scene text detection with graph convolutional networks10.1016/j.patcog.2020.107684 ↗
25. Nagaoka, Y., Miyazaki, T., Sugaya, Y., and Omachi, S. (2021). Text Detection Using Multi-Stage Region Proposal Network Sensitive to Text Scale †. Sensors, 21.10.3390/s21041232 ↗
26. Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv.
27. Yu, F., and Koltun, V. (2015). Multi-Scale Context Aggregation by Dilated Convolutions. ICLR. arXiv.
28. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27–30). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.308 ↗
29. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7–12). Going Deeper with Convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.10.1109/CVPR.2015.7298594 ↗
30. Focal Loss for Dense Object Detection10.1109/TPAMI.2018.2858826 ↗
31. Yao, C., Bai, X., Sang, N., Zhou, X., Zhou, S., and Cao, Z. (2016). Scene Text Detection via Holistic, Multi-Channel Prediction. arXiv.
32. Deep Neural Networks Combined with STN for Multi-Oriented Text Detection and Recognition
33. Total-Text: Toward Orientation Robustness in Scene Text Detection10.1007/s10032-019-00334-z ↗
34. RFRN: A Recurrent Feature Refinement Network for Accurate and Efficient Scene Text Detection10.1016/j.neucom.2020.10.099 ↗