{"type":"ScholarlyArticle","title":"Orientation-Independent Chinese Text Recognition in Scene Images","authors":[{"name":"Haiyang Yu","slug":"haiyang-yu","given":"Haiyang","family":"Yu","affiliation":"Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science , Fudan University"},{"name":"Xiaocong Wang","slug":"xiaocong-wang","given":"Xiaocong","family":"Wang","affiliation":"Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science , Fudan University"},{"name":"Bin Li","slug":"bin-li","given":"Bin","family":"Li","affiliation":"Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science , Fudan University"},{"name":"Xiangyang Xue","slug":"xiangyang-xue","given":"Xiangyang","family":"Xue","affiliation":"Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science , Fudan University"}],"abstract":"Scene text recognition (STR) has attracted much attention due to its broad applications. The previous works pay more attention to dealing with the recognition of Latin text images with complex backgrounds by introducing language models or other auxiliary networks. Different from Latin texts, many vertical Chinese texts exist in natural scenes, which brings difficulties to current state-of-the-art STR methods. In this paper, we take the first attempt to extract orientation-independent visual features by disentangling content and orientation information of text images, thus recognizing both horizontal and vertical texts robustly in natural scenes. Specifically, we introduce a Character Image Reconstruction Network (CIRN) to recover corresponding printed character images with disentangled content and orientation information. We conduct experiments on a scene dataset for benchmarking Chinese text recognition, and the results demonstrate that the proposed method can indeed improve performance through disentangling content and orientation information. To further validate the effectiveness of our method, we additionally collect a Vertical Chinese Text Recognition (VCTR) dataset. The experimental results show that the proposed method achieves 45.63\\% improvement on VCTR when introducing CIRN to the baseline model.","doi":"10.24963/ijcai.2023/185","url":"https://ijcai.org/proceedings/2023/185","pdf":"https://ijcai.org/proceedings/2023/0185.pdf","markdown":"https://ijcai.org/proceedings/2023/185.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/185","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":185,"pages":{"first":1667,"last":1675},"keywords":["Computer Vision: CV: Vision and language","Computer Vision: CV: Recognition (object detection, categorization)"],"videos":[],"references":[{"position":1,"raw":"[Cao et al., 2020] Zhong Cao, Jiang Lu, Sen Cui, and Changshui Zhang. Zero-shot handwritten chinese char- acter recognition with hierarchical decomposition embed- ding. Pattern Recognition, 107:107488, 2020.","title":"Zero-shot handwritten chinese character recognition with hierarchical decomposition embedding","authors":"Zhong Cao, Jiang Lu, Sen Cui, Changshui Zhang","year":"2020","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2021a] Jingye Chen, Bin Li, and Xiangyang Xue. Scene text telescope: Text-focused scene image super-resolution. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 12026-12035, 2021.","title":"Scene text telescope: Text-focused scene image super-resolution","authors":"Chen","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2021b] Jingye Chen, Bin Li, and Xiangyang Xue. Zero-shot chinese character recognition with stroke- level decomposition. International Joint Conference on Artificial Intelligence, 2021.","title":"Zero-shot chinese character recognition with strokelevel decomposition","authors":"Chen","year":"2021","venue":"International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2021c] Jingye Chen, Haiyang Yu, Jianqi Ma, Mengnan Guan, Xixi Xu, Xiaocong Wang, Shaobo Qu, Bin Li, and Xiangyang Xue. Benchmarking chinese text recognition: Datasets, baselines, and an empirical study. arXiv preprint arXiv:2112.15093, 2021.","title":"Benchmarking chinese text recognition: Datasets, baselines, and an empirical study","authors":"Chen","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cheng et al., 2017] Zhanzhan Cheng, Fan Bai, Yunlu Xu, Gang Zheng, Shiliang Pu, and Shuigeng Zhou. Focusing attention: Towards accurate text recognition in natural im- ages. In Proceedings of the IEEE international conference on computer vision, pages 5076-5084, 2017.","title":"Focusing attention: Towards accurate text recognition in natural images","authors":"Zhanzhan Cheng, Fan Bai, Yunlu Xu, Gang Zheng, Shiliang Pu, Shuigeng Zhou","year":"2017","venue":"Proceedings of the IEEE international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chng et al., 2019] Chee Kheng Chng, Yuliang Liu, Yipeng Sun, Chun Chet Ng, Canjie Luo, Zihan Ni, ChuanMing Fang, Shuaitao Zhang, Junyu Han, Errui Ding, et al. Icdar2019 robust reading challenge on arbitrary-shaped text-rrc-art. In 2019 International Conference on Doc- ument Analysis and Recognition (ICDAR), pages 1571- 1576. IEEE, 2019.","title":"Icdar2019 robust reading challenge on arbitrary-shaped text-rrc-art","authors":"Chee Kheng, Chng, Yuliang Liu, Yipeng Sun, Chun, Chet Ng, Canjie Luo, Zihan Ni, Chuanming Fang, Shuaitao Zhang, Junyu Han, Errui Ding","year":"2019","venue":"2019 International Conference on Document Analysis and Recognition (ICDAR)","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Du et al., 2022] Yongkun Du, Zhineng Chen, Caiyan Jia, Xiaoting Yin, Tianlun Zheng, Chenxia Li, Yuning Du, and Yu-Gang Jiang. Svtr: Scene text recognition with a single visual model. arXiv preprint arXiv:2205.00159, 2022.","title":"Svtr: Scene text recognition with a single visual model","authors":"Du","year":"2022","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2022/124"},{"position":8,"raw":"[Fang et al., 2021] Shancheng Fang, Hongtao Xie, Yuxin Wang, Zhendong Mao, and Yongdong Zhang. Read like humans: Autonomous, bidirectional and iterative language modeling for scene text recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 7098-7107, 2021.","title":"Read like humans: Autonomous, bidirectional and iterative language modeling for scene text recognition","authors":"Fang","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Graves et al., 2006] Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In Proceedings of the 23rd international conference on Machine learning, pages 369-376, 2006.","title":"Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks","authors":"Alex Graves, Santiago Fernández, Faustino Gomez, Jürgen Schmidhuber","year":"2006","venue":"Proceedings of the 23rd international conference on Machine learning","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Jaderberg et al., 2015] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. Advances in neural information processing systems, 28, 2015.","title":"Spatial transformer networks","authors":"Jaderberg","year":"2015","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Jiang et al., 2022] Gangwei Jiang, Shiyao Wang, Tiezheng Ge, Yuning Jiang, Ying Wei, and Defu Lian. Self- supervised text erasing with controllable image synthesis. arXiv preprint arXiv:2204.12743, 2022.","title":"Selfsupervised text erasing with controllable image synthesis","authors":"Jiang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Laptev et al., 2016] Dmitry Laptev, Nikolay Savinov, Joachim M Buhmann, and Marc Pollefeys. Ti-pooling: transformation-invariant pooling for feature learning in convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 289-297, 2016.","title":"Ti-pooling: transformation-invariant pooling for feature learning in convolutional neural networks","authors":"Dmitry Laptev, Nikolay Savinov, Joachim M Buhmann, Marc Pollefeys","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Li et al., 2019] Hui Li, Peng Wang, Chunhua Shen, and Guyu Zhang. Show, attend and read: A simple and strong baseline for irregular text recognition. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 8610-8617, 2019.","title":"Show, attend and read: A simple and strong baseline for irregular text recognition","authors":"Hui Li, Peng Wang, Chunhua Shen, Guyu Zhang","year":"2019","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liao et al., 2019] Minghui Liao, Jian Zhang, Zhaoyi Wan, Fengming Xie, Jiajun Liang, Pengyuan Lyu, Cong Yao, and Xiang Bai. Scene text recognition from two- dimensional perspective. In Proceedings of the AAAI Con- ference on Artificial Intelligence, volume 33, pages 8714- 8721, 2019.","title":"Scene text recognition from twodimensional perspective","authors":"Liao","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Liu et al., 2018] Wei Liu, Chaofeng Chen, and Kwan-Yee Wong. Char-net: A character-aware neural network for distorted scene text recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.","title":"Char-net: A character-aware neural network for distorted scene text recognition","authors":"Wei Liu, Chaofeng Chen, Kwan-Yee Wong","year":"2018","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Luo et al., 2019] Canjie Luo, Lianwen Jin, and Zenghui Sun. Moran: A multi-object rectified attention network for scene text recognition. Pattern Recognition, 90:109-118, 2019.","title":"Moran: A multi-object rectified attention network for scene text recognition","authors":"Luo","year":"2019","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Neumann and Matas, 2012] Lukas Neumann and Jiri Matas. Real-time scene text localization and recognition. Computer Vision and Pattern Recognition, 2012.","title":"Real-time scene text localization and recognition","authors":"Matas Neumann, Lukas Neumann, Jiri Matas","year":"2012","venue":"Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Qiao et al., 2020] Zhi Qiao, Yu Zhou, Dongbao Yang, Yu- can Zhou, and Weiping Wang. Seed: Semantics en- hanced encoder-decoder framework for scene text recog- nition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13528- 13537, 2020.","title":"Seed: Semantics enhanced encoder-decoder framework for scene text recognition","authors":"Zhi Qiao, Yu Zhou, Dongbao Yang, Yucan Zhou, Weiping Wang","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Sermanet and LeCun, 2011] Pierre Sermanet and Yann Le- Cun. Traffic sign recognition with multi-scale convolu- tional networks. In The 2011 international joint confer- ence on neural networks, pages 2809-2813. IEEE, 2011.","title":"Traffic sign recognition with multi-scale convolutional networks","authors":"Lecun Sermanet, Pierre Sermanet, Yann Le-Cun","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Shi et al., 2016] Baoguang Shi, Xiang Bai, and Cong Yao. 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, 39(11):2298-2304, 2016.","title":"An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition","authors":"Shi","year":"2016","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Shi et al., 2017] Baoguang Shi, Cong Yao, Minghui Liao, Mingkun Yang, Pei Xu, Linyan Cui, Serge Belongie, Shi- jian Lu, and Xiang Bai. Icdar2017 competition on reading chinese text in the wild (rctw-17). In 2017 14th iapr inter- national conference on document analysis and recognition (ICDAR), volume 1, pages 1429-1434. IEEE, 2017.","title":"Icdar2017 competition on reading chinese text in the wild (rctw-17)","authors":"Baoguang Shi, Cong Yao, Minghui Liao, Mingkun Yang, Pei Xu, Linyan Cui, Serge Belongie, Shijian Lu, Xiang Bai","year":"2017","venue":"2017 14th iapr international conference on document analysis and recognition (ICDAR)","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Shi et al., 2018] Baoguang Shi, Mingkun Yang, Xinggang Wang, Pengyuan Lyu, Cong Yao, and Xiang Bai. Aster: An attentional scene text recognizer with flexible rectifica- tion. IEEE transactions on pattern analysis and machine intelligence, 41(9):2035-2048, 2018.","title":"Aster: An attentional scene text recognizer with flexible rectification","authors":"Shi","year":"2018","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Su et al., 2023] Shangchao Su, Haiyang Yu, Bin Li, and Xi- angyang Xue. Privacy-preserving collaborative chinese text recognition with federated learning. arXiv preprint arXiv:2305.05602, 2023.","title":"Privacy-preserving collaborative chinese text recognition with federated learning","authors":"Su","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Sun et al., 2019] Yipeng Sun, Zihan Ni, Chee-Kheng Chng, Yuliang Liu, Canjie Luo, Chun Chet Ng, Junyu Han, Er- rui Ding, Jingtuo Liu, Dimosthenis Karatzas, et al. Ic- dar 2019 competition on large-scale street view text with partial labeling-rrc-lsvt. In 2019 International Conference on Document Analysis and Recognition (ICDAR), pages 1557-1562. IEEE, 2019.","title":"Icdar 2019 competition on large-scale street view text with partial labeling-rrc-lsvt","authors":"Yipeng Sun, Zihan Ni, Chee-Kheng Chng, Yuliang Liu, Canjie Luo, Chun, Chet Ng, Junyu Han, Errui Ding, Jingtuo Liu, Dimosthenis Karatzas","year":"2019","venue":"2019 International Conference on Document Analysis and Recognition (ICDAR)","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wan et al., 2020] Zhaoyi Wan, Minghang He, Haoran Chen, Xiang Bai, and Cong Yao. Textscanner: Reading characters in order for robust scene text recognition. Pro- ceedings of the AAAI Conference on Artificial Intelligence, 2020.","title":"Textscanner: Reading characters in order for robust scene text recognition","authors":"Wan","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wang et al., 2011] Kai Wang, Boris Babenko, and Serge Belongie. End-to-end scene text recognition. International Conference on Computer Vision, 2011.","title":"End-to-end scene text recognition","authors":"Kai Wang, Boris Babenko, Serge Belongie","year":"2011","venue":"International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2018] Wenchao Wang, Jianshu Zhang, Jun Du, Zi-Rui Wang, and Yixing Zhu. Denseran for offline hand- written chinese character recognition. International Con- ference on Frontiers in Handwriting Recognition, 2018.","title":"Denseran for offline handwritten chinese character recognition","authors":"Wenchao Wang, Jianshu Zhang, Jun Du, Zi-Rui Wang, Yixing Zhu","year":"2018","venue":"International Conference on Frontiers in Handwriting Recognition","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wang et al., 2019] Zihao Wang, Xihui Liu, Hongsheng Li, Lu Sheng, Junjie Yan, Xiaogang Wang, and Jing Shao. Camp: Cross-modal adaptive message passing for text- image retrieval. In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pages 5764-5773, 2019.","title":"Camp: Cross-modal adaptive message passing for textimage retrieval","authors":"Wang","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wang et al., 2021] Yuxin Wang, Hongtao Xie, Shancheng Fang, Jing Wang, Shenggao Zhu, and Yongdong Zhang. From two to one: A new scene text recognizer with vi- sual language modeling network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14194-14203, 2021.","title":"From two to one: A new scene text recognizer with visual language modeling network","authors":"Yuxin Wang, Hongtao Xie, Shancheng Fang, Jing Wang, Shenggao Zhu, Yongdong Zhang","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wu et al., 2019] Changjie Wu, Zi-Rui Wang, Jun Du, Jian- shu Zhang, and Jiaming Wang. Joint spatial and radical analysis network for distorted chinese character recogni- tion. In 2019 International Conference on Document Anal- ysis and Recognition Workshops (ICDARW), volume 5, pages 122-127. IEEE, 2019.","title":"Joint spatial and radical analysis network for distorted chinese character recognition","authors":"Wu","year":"2019","venue":"International Conference on Document Analysis and Recognition Workshops (ICDARW)","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Yang et al., 2019] Mingkun Yang, Yushuo Guan, Minghui Liao, Xin He, Kaigui Bian, Song Bai, Cong Yao, and Xi- ang Bai. Symmetry-constrained rectification network for scene text recognition. International Conference on Com- puter Vision, 2019.","title":"Symmetry-constrained rectification network for scene text recognition","authors":"Yang","year":"2019","venue":"International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Yin et al., 2017] Fei Yin, Yi-Chao Wu, Xu-Yao Zhang, and Cheng-Lin Liu. Scene text recognition with slid- ing convolutional character models. arXiv preprint arXiv:1709.01727, 2017.","title":"Scene text recognition with sliding convolutional character models","authors":"Fei Yin, Yi-Chao Wu, Xu-Yao Zhang, Cheng-Lin Liu","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Yu et al., 2020] Deli Yu, Xuan Li, Chengquan Zhang, Tao Liu, Junyu Han, Jingtuo Liu, and Errui Ding. Towards ac- curate scene text recognition with semantic reasoning net- works. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12113- 12122, 2020.","title":"Towards accurate scene text recognition with semantic reasoning networks","authors":"Deli Yu, Xuan Li, Chengquan Zhang, Tao Liu, Junyu Han, Jingtuo Liu, Errui Ding","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Yu et al., 2022] Haiyang Yu, Jingye Chen, Bin Li, and Xi- angyang Xue. Chinese character recognition with radical- structured stroke trees. arXiv preprint arXiv:2211.13518, 2022.","title":"Chinese character recognition with radicalstructured stroke trees","authors":"Haiyang Yu, Jingye Chen, Bin Li, Xiangyang Xue","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Yuan et al., 2019] Tai-Ling Yuan, Zhe Zhu, Kun Xu, Cheng-Jun Li, Tai-Jiang Mu, and Shi-Min Hu. A large chinese text dataset in the wild. Journal of Computer Sci- ence and Technology, 34(3):509-521, 2019.","title":"A large chinese text dataset in the wild","authors":"Tai-Ling Yuan, Zhe Zhu, Kun Xu, Cheng-Jun Li, Tai-Jiang Mu, Shi-Min Hu","year":"2019","venue":"Journal of Computer Science and Technology","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zeiler, 2012] Matthew D Zeiler. Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701, 2012.","title":"Adadelta: an adaptive learning rate method","authors":"D Matthew, Zeiler","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhan and Lu, 2019] Fangneng Zhan and Shijian Lu. Esir: End-to-end scene text recognition via iterative image rec- tification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2059- 2068, 2019.","title":"Esir: End-to-end scene text recognition via iterative image rectification","authors":"Lu ; Zhan, Fangneng Zhan, Shijian Lu","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhang et al., 2019] Rui Zhang, Yongsheng Zhou, Qianyi Jiang, Qi Song, Nan Li, Kai Zhou, Lei Wang, Dong Wang, Minghui Liao, Mingkun Yang, et al. Icdar 2019 robust reading challenge on reading chinese text on signboard. In 2019 international conference on document analysis and recognition (ICDAR), pages 1577-1581. IEEE, 2019.","title":"Icdar 2019 robust reading challenge on reading chinese text on signboard","authors":"Rui Zhang, Yongsheng Zhou, Qianyi Jiang, Qi Song, Nan Li, Kai Zhou, Lei Wang, Dong Wang, Minghui Liao, Mingkun Yang","year":"2019","venue":"2019 international conference on document analysis and recognition (ICDAR)","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zu et al., 2022] Xinyan Zu, Haiyang Yu, Bin Li, and Xi- angyang Xue. Chinese character recognition with aug- mented character profile matching. In Proceedings of the 30th ACM International Conference on Multimedia, pages 6094-6102, 2022.","title":"Chinese character recognition with augmented character profile matching","authors":"Xinyan Zu, Haiyang Yu, Bin Li, Xiangyang Xue","year":"2022","venue":"Proceedings of the 30th ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}