Exploring Anchor-Free Approach for Reading Chinese Characters
Pages 23 - 28
Abstract
Scene text spotting has achieved an impressive performance over recent years. Currently, most text localization methods are designed with the text line instance. We argue that building a character-level spotting network is more suited to recognize the Chinese of text and Chinese is also common in scene text images. In this paper, we explore an anchor-free spotting framework that treats a character as a single point. To better capture Chinese character features, we first use the Canny edge detectors and superimpose the obtained edge information onto the RGB image channel. After that, a feed-forward network is set up and the inference can be processed in a single network forward-pass, without complex post-processing steps. Experiments are performed on the Chinese text dataset and the quantitative comparisons demonstrate the effectiveness of the anchor-free approach.
References
[1]
David Acuna, Amlan Kar, and Sanja Fidler. 2019. Devil is in the edges: Learning semantic boundaries from noisy annotations. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11075--11083.
[2]
Youngmin Baek, Bado Lee, Dongyoon Han, Sangdoo Yun, and Hwalsuk Lee. 2019. Character region awareness for text detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9365--9374.
[3]
Youngmin Baek, Seung Shin, Jeonghun Baek, Sungrae Park, Junyeop Lee, Daehyun Nam, and Hwalsuk Lee. 2020. Character region attention for text spotting. In European Conference on Computer Vision. 504--521.
[4]
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. 2017. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, 4 (2017), 834--848.
[5]
Ying Chen, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Xi Li. 2022. Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting. In European Conference on Computer Vision. 356--373.
[6]
Lijun Ding and Ardeshir Goshtasby. 2001. On the Canny edge detector. Pattern Recognition, Vol. 34, 3 (2001), 721--725.
[7]
Zhichao Fu, Yingbin Zheng, Tianlong Ma, Hao Ye, Jing Yang, and Liang He. 2022. Edge-aware deep image deblurring. Neurocomputing, Vol. 502 (2022), 37--47.
[8]
Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph. 2021. Simple copy-paste is a strong data augmentation method for instance segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2918--2928.
[9]
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. 2017. Mask r-cnn. In IEEE/CVF International Conference on Computer Vision. 2961--2969.
[10]
Mingxin Huang, Yuliang Liu, Zhenghao Peng, Chongyu Liu, Dahua Lin, Shenggao Zhu, Nicholas Yuan, Kai Ding, and Lianwen Jin. 2022. SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4593--4603.
[11]
Minghui Liao, Guan Pang, Jing Huang, Tal Hassner, and Xiang Bai. 2020. Mask textspotter v3: Segmentation proposal network for robust scene text spotting. In European Conference on Computer Vision. 706--722.
[12]
Minghui Liao, Baoguang Shi, Xiang Bai, Xinggang Wang, and Wenyu Liu. 2017. Textboxes: A fast text detector with a single deep neural network. In AAAI Conference on Artificial Intelligence.
[13]
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. 2016. Ssd: Single shot multibox detector. In European Conference on Computer Vision. 21--37.
[14]
Xuebo Liu, Ding Liang, Shi Yan, Dagui Chen, Yu Qiao, and Junjie Yan. 2018. Fots: Fast oriented text spotting with a unified network. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5676--5685.
[15]
Yuliang Liu, Chunhua Shen, Lianwen Jin, Tong He, Peng Chen, Chongyu Liu, and Hao Chen. 2022. Abcnet v2: Adaptive bezier-curve network for real-time end-to-end text spotting. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, 11 (2022), 8048--8064.
[16]
Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3431--3440.
[17]
Pengyuan Lyu, Minghui Liao, Cong Yao, Wenhao Wu, and Xiang Bai. 2018. Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes. In European Conference on Computer Vision. 67--83.
[18]
Jianqi Ma, Weiyuan Shao, Hao Ye, Li Wang, Hong Wang, Yingbin Zheng, and Xiangyang Xue. 2018. Arbitrary-oriented scene text detection via rotation proposals. IEEE Transactions on Multimedia, Vol. 20, 11 (2018), 3111--3122.
[19]
G Mandal and D Bhattacharjee. 2020. Learning-based single image super-resolution with improved edge information. Pattern Recognition and Image Analysis, Vol. 30, 3 (2020), 391--400.
[20]
Dezhi Peng, Xinyu Wang, Yuliang Liu, Jiaxin Zhang, Mingxin Huang, Songxuan Lai, Jing Li, Shenggao Zhu, Dahua Lin, Chunhua Shen, et al. 2022. SPTS: single-point text spotting. 4272--4281.
[21]
Liang Qiao, Ying Chen, Zhanzhan Cheng, Yunlu Xu, Yi Niu, Shiliang Pu, and Fei Wu. 2021. Mango: A mask attention guided one-stage scene text spotter. In AAAI Conference on Artificial Intelligence, Vol. 35. 2467--2476.
[22]
Siyang Qin, Alessandro Bissacco, Michalis Raptis, Yasuhisa Fujii, and Ying Xiao. 2019. Towards unconstrained end-to-end text spotting. In IEEE/CVF International Conference on Computer Vision. 4704--4714.
[23]
Joseph Redmon and Ali Farhadi. 2017. YOLO9000: better, faster, stronger. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7263--7271.
[24]
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. Neural Information Processing Systems, Vol. 28 (2015), 91--99.
[25]
Baoguang Shi, Xiang Bai, and Cong Yao. 2016. An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, 11 (2016), 2298--2304.
[26]
Towaki Takikawa, David Acuna, Varun Jampani, and Sanja Fidler. 2019. Gated-scnn: Gated shape cnns for semantic segmentation. In IEEE/CVF International Conference on Computer Vision. 5229--5238.
[27]
Hao Wang, Pu Lu, Hui Zhang, Mingkun Yang, Xiang Bai, Yongchao Xu, Mengchao He, Yongpan Wang, and Wenyu Liu. 2020. All you need is boundary: Toward arbitrary-shaped text spotting. In AAAI Conference on Artificial Intelligence, Vol. 34. 12160--12167.
[28]
Tianwei Wang, Yuanzhi Zhu, Lianwen Jin, Dezhi Peng, Zhe Li, Mengchao He, Yongpan Wang, and Canjie Luo. 2021b. Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5973--5982.
[29]
Wenhai Wang, Enze Xie, Xiang Li, Xuebo Liu, Ding Liang, Zhibo Yang, Tong Lu, and Chunhua Shen. 2021a. Pan: Towards efficient and accurate end-to-end spotting of arbitrarily-shaped text. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, 9 (2021), 5349--5367.
[30]
Xingjiao Wu, Yingbin Zheng, Tianlong Ma, Hao Ye, and Liang He. 2021. Document image layout analysis via explicit edge embedding network. Information Sciences, Vol. 577 (2021), 436--448.
[31]
Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell. 2018. Deep layer aggregation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2403--2412.
[32]
Tai-Ling Yuan, Zhe Zhu, Kun Xu, Cheng-Jun Li, Tai-Jiang Mu, and Shi-Min Hu. 2019. A large chinese text dataset in the wild. Journal of Computer Science and Technology, Vol. 34, 3 (2019), 509--521.
[33]
Humen Zhong, Jun Tang, Wenhai Wang, Zhibo Yang, Cong Yao, and Tong Lu. 2021. Arts: Eliminating inconsistency between text detection and recognition with auto-rectification text spotter. arXiv:2110.10405 (2021).
[34]
Xingyi Zhou, Dequan Wang, and Philipp Kr"ahenbühl. 2019. Objects as points. arXiv:1904.07850 (2019).
[35]
Xinyu Zhou, Cong Yao, He Wen, Yuzhi Wang, Shuchang Zhou, Weiran He, and Jiajun Liang. 2017. East: an efficient and accurate scene text detector. In IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5551--5560. io
Index Terms
- Exploring Anchor-Free Approach for Reading Chinese Characters
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October 2023
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Published: 29 October 2023
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