{"type":"ScholarlyArticle","title":"Focus on the Whole Character: Discriminative Character Modeling for Scene Text Recognition","authors":[{"name":"Bangbang Zhou","slug":"bangbang-zhou","given":"Bangbang","family":"Zhou","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Yadong Qu","slug":"yadong-qu","given":"Yadong","family":"Qu","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Zixiao Wang","slug":"zixiao-wang","given":"Zixiao","family":"Wang","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Zicheng Li","slug":"zicheng-li","given":"Zicheng","family":"Li","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Boqiang Zhang","slug":"boqiang-zhang","given":"Boqiang","family":"Zhang","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Hongtao Xie","slug":"hongtao-xie","given":"Hongtao","family":"Xie","affiliation":"University of Science and Technology of China , Hefei , China"}],"abstract":"Recently, scene text recognition (STR) models have shown significant performance improvements.  However, existing models still encounter difficulties in recognizing challenging texts that involve factors such as severely distorted and perspective characters. These challenging texts mainly cause two problems: (1) Large Intra-Class Variance. (2) Small Inter-Class Variance. An extremely distorted character may prominently differ visually from other characters within the same category, while the variance between characters from different classes is relatively small. To address the above issues, we propose a novel method that enriches the character features to enhance the discriminability of characters. Firstly, we propose the Character-Aware Constraint Encoder (CACE) with multiple blocks stacked. CACE introduces a decay matrix in each block to explicitly guide the attention region for each token. By continuously employing the decay matrix, CACE enables tokens to perceive morphological information at the character level. Secondly, an Intra-Inter Consistency Loss (I^2CL) is introduced to consider intra-class compactness and inter-class separability at feature space. I^2CL improves the discriminative capability of features by learning a long-term memory unit for each character category. Trained with synthetic data, our model achieves state-of-the-art performance on common benchmarks (94.1% accuracy) and Union14M-Benchmark (61.6% accuracy). Code is available at https://github.com/bang123-box/CFE.","doi":"10.24963/ijcai.2024/195","url":"https://ijcai.org/proceedings/2024/195","pdf":"https://ijcai.org/proceedings/2024/0195.pdf","markdown":"https://ijcai.org/proceedings/2024/195.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/195","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":195,"pages":{"first":1762,"last":1770},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Multimodal learning"],"videos":[],"references":[{"position":1,"raw":"[Atienza, 2021] Rowel Atienza. Vision transformer for fast and efficient scene text recognition. In International Con- ference on Document Analysis and Recognition, pages 319-334. Springer, 2021.","title":"Vision transformer for fast and efficient scene text recognition","authors":"Rowel Atienza","year":"2021","venue":"International Conference on Document Analysis and Recognition","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Baek et al., 2019] Jeonghun Baek, Geewook Kim, Jun- yeop Lee, Sungrae Park, Dongyoon Han, Sangdoo Yun, Seong Joon Oh, and Hwalsuk Lee. What is wrong with scene text recognition model comparisons? dataset and model analysis. In International Conference on Computer Vision (ICCV), 2019.","title":"What is wrong with scene text recognition model comparisons? dataset and model analysis","authors":"Baek","year":"2019","venue":"International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Baek et al., 2021] Jeonghun Baek, Yusuke Matsui, and Kiy- oharu Aizawa. What if we only use real datasets for scene text recognition? toward scene text recognition with fewer labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3113- 3122, 2021.","title":"What if we only use real datasets for scene text recognition? toward scene text recognition with fewer labels","authors":"Baek","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bautista and Atienza, 2022] Darwin Bautista and Rowel Atienza. Scene text recognition with permuted autoregres- sive sequence models. In European Conference on Com- puter Vision, pages 178-196. Springer, 2022.","title":"Darwin Bautista and Rowel Atienza. Scene text recognition with permuted autoregressive sequence models","authors":"Atienza Bautista","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cheng et al., 2023] Changxu Cheng, Peng Wang, Cheng Da, Qi Zheng, and Cong Yao. Lister: Neighbor decoding for length-insensitive scene text recognition. In Proceed- ings of the IEEE/CVF International Conference on Com- puter Vision, pages 19541-19551, 2023.","title":"Lister: Neighbor decoding for length-insensitive scene text recognition","authors":"Changxu Cheng, Peng Wang, Cheng Da, Qi Zheng, Cong Yao","year":"2023","venue":"Proceedings of the IEEE/CVF 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":"[Fan et al., 2023] Qihang Fan, Huaibo Huang, Mingrui Chen, Hongmin Liu, and Ran He. Rmt: Reten- tive networks meet vision transformers. arXiv preprint arXiv:2309.11523, 2023.","title":"Rmt: Retentive networks meet vision transformers","authors":"Fan","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"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":"Shancheng Fang, Hongtao Xie, Yuxin Wang, Zhendong Mao, Yongdong Zhang","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":10,"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":"Graves","year":"2006","venue":"Proceedings of the 23rd international conference on Machine learning","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gupta et al., 2016] Ankush Gupta, Andrea Vedaldi, and An- drew Zisserman. Synthetic data for text localisation in natural images. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2315- 2324, 2016.","title":"Synthetic data for text localisation in natural images","authors":"Ankush Gupta, Andrea Vedaldi, Andrew Zisserman","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Jaderberg et al., 2014] Max Jaderberg, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Synthetic data and artificial neural networks for natural scene text recog- nition. arXiv preprint arXiv:1406.2227, 2014.","title":"Synthetic data and artificial neural networks for natural scene text recognition","authors":"Jaderberg","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Jaderberg et al., 2016] Max Jaderberg, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Reading text in the wild with convolutional neural networks. International journal of computer vision, 116(1):1-20, 2016.","title":"Reading text in the wild with convolutional neural networks","authors":"Jaderberg","year":"2016","venue":"International journal of computer vision","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Jiang et al., 2023] Qing Jiang, Jiapeng Wang, Dezhi Peng, Chongyu Liu, and Lianwen Jin. Revisiting scene text recognition: A data perspective. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 20543-20554, 2023.","title":"Revisiting scene text recognition: A data perspective","authors":"Qing Jiang, Jiapeng Wang, Dezhi Peng, Chongyu Liu, Lianwen Jin","year":"2023","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Karatzas et al., 2013] Dimosthenis Karatzas, Faisal Shafait, Seiichi Uchida, Masakazu Iwamura, Lluis Gomez i Big- orda, Sergi Robles Mestre, Joan Mas, David Fer- nandez Mota, Jon Almazan Almazan, and Lluis Pere De Las Heras. Icdar 2013 robust reading competition. In 2013 12th international conference on document analysis and recognition, pages 1484-1493. IEEE, 2013.","title":"Icdar 2013 robust reading competition","authors":"Dimosthenis Karatzas, Faisal Shafait, Seiichi Uchida, Masakazu Iwamura, Lluis Gomez I Bigorda, Sergi Robles Mestre, Joan Mas, David Fernandez Mota, Jon Almazan Almazan, Lluis Pere De, Las Heras","year":"2013","venue":"2013 12th international conference on document analysis and recognition","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Karatzas et al., 2015] Dimosthenis Karatzas, Lluis Gomez- Bigorda, Anguelos Nicolaou, Suman Ghosh, Andrew Bag- danov, Masakazu Iwamura, Jiri Matas, Lukas Neumann, Vijay Ramaseshan Chandrasekhar, Shijian Lu, et al. Icdar 2015 competition on robust reading. In 2015 13th interna- tional conference on document analysis and recognition (ICDAR), pages 1156-1160. IEEE, 2015.","title":"Icdar 2015 competition on robust reading","authors":"Dimosthenis Karatzas, Lluis Gomez-Bigorda, Anguelos Nicolaou, Suman Ghosh, Andrew Bagdanov, Masakazu Iwamura, Jiri Matas, Lukas Neumann, Vijay Ramaseshan Chandrasekhar, Shijian Lu","year":"2015","venue":"2015 13th international conference on document analysis and recognition (ICDAR)","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kingma and Ba, 2014] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Mishra et al., 2012] Anand Mishra, Karteek Alahari, and CV Jawahar. Scene text recognition using higher order language priors. In BMVC-British machine vision confer- ence. BMVA, 2012.","title":"Scene text recognition using higher order language priors","authors":"Anand Mishra, Karteek Alahari, Jawahar","year":"2012","venue":"BMVC-British machine vision conference","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Na et al., 2022] Byeonghu Na, Yoonsik Kim, and Sungrae Park. Multi-modal text recognition networks: Interac- tive enhancements between visual and semantic features. In European Conference on Computer Vision, pages 446- 463. Springer, 2022.","title":"Sungrae Park. Multi-modal text recognition networks: Interactive enhancements between visual and semantic features","authors":"Na","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Phan et al., 2013] Trung Quy Phan, Palaiahnakote Shivaku- mara, Shangxuan Tian, and Chew Lim Tan. Recognizing text with perspective distortion in natural scenes. In Pro- ceedings of the IEEE International Conference on Com- puter Vision, pages 569-576, 2013.","title":"Recognizing text with perspective distortion in natural scenes","authors":"Trung Quy Phan, Palaiahnakote Shivakumara, Shangxuan Tian, Chew Lim, Tan","year":"2013","venue":"Proceedings of the IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Qi and Su, 2017] Ce Qi and Fei Su. Contrastive-center loss for deep neural networks. In 2017 IEEE international conference on image processing (ICIP), pages 2851-2855. IEEE, 2017.","title":"Contrastive-center loss for deep neural networks","authors":"Ce Qi, Fei Su","year":"2017","venue":"IEEE international conference on image processing (ICIP)","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Risnumawan et al., 2014] Anhar Risnumawan, Palaia- hankote Shivakumara, Chee Seng Chan, and Chew Lim Tan. A robust arbitrary text detection system for nat- ural scene images. Expert Systems with Applications, 41(18):8027-8048, 2014.","title":"A robust arbitrary text detection system for natural scene images","authors":"Anhar Risnumawan, Palaiahankote Shivakumara, Chee Seng Chan, Chew Lim, Tan","year":"2014","venue":"Expert Systems with Applications","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Sheng et al., 2019] Fenfen Sheng, Zhineng Chen, and Bo Xu. Nrtr: A no-recurrence sequence-to-sequence model for scene text recognition. In 2019 International conference on document analysis and recognition (IC- DAR), pages 781-786. IEEE, 2019.","title":"Nrtr: A no-recurrence sequence-to-sequence model for scene text recognition","authors":"Sheng","year":"2019","venue":"2019 International conference on document analysis and recognition (IC-DAR)","doi":null,"url":null,"internal_url":null},{"position":24,"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":25,"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":26,"raw":"[Sun et al., 2023] Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, and Furu Wei. Retentive network: A successor to transformer for large language models. arXiv preprint arXiv:2307.08621, 2023.","title":"Retentive network: A successor to transformer for large language models","authors":"Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, Furu Wei","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Van der Maaten and Hinton, 2008] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008.","title":"Visualizing data using t-sne","authors":"Van Der Maaten, Laurens Hinton, Geoffrey Van Der Maaten, Hinton","year":"2008","venue":"Journal of machine learning research","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Veit et al., 2016] Andreas Veit, Tomas Matera, Lukas Neu- mann, Jiri Matas, and Serge Belongie. Coco-text: Dataset and benchmark for text detection and recognition in natu- ral images. arXiv preprint arXiv:1601.07140, 2016.","title":"Coco-text: Dataset and benchmark for text detection and recognition in natural images","authors":"Andreas Veit, Tomas Matera, Lukas Neumann, Jiri Matas, Serge Belongie","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2011] Kai Wang, Boris Babenko, and Serge Belongie. End-to-end scene text recognition. In 2011 In- ternational conference on computer vision, pages 1457- 1464. IEEE, 2011.","title":"End-to-end scene text recognition","authors":"Kai Wang, Boris Babenko, Serge Belongie","year":"2011","venue":"2011 International conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":30,"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":31,"raw":"[Wang et al., 2022] Peng Wang, Cheng Da, and Cong Yao. Multi-granularity prediction for scene text recognition. In European Conference on Computer Vision, pages 339- 355. Springer, 2022.","title":"Multi-granularity prediction for scene text recognition","authors":"Peng Wang, Cheng Da, Cong Yao","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wang et al., 2023] Zixiao Wang, Hongtao Xie, Yuxin Wang, Jianjun Xu, Boqiang Zhang, and Yongdong Zhang. Symmetrical linguistic feature distillation with clip for scene text recognition. In Proceedings of the 31st ACM International Conference on Multimedia, pages 509-518, 2023.","title":"Symmetrical linguistic feature distillation with clip for scene text recognition","authors":"Wang","year":"2023","venue":"Proceedings of the 31st ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Xie et al., 2022] Xudong Xie, Ling Fu, Zhifei Zhang, Zhaowen Wang, and Xiang Bai. Toward understanding wordart: Corner-guided transformer for scene text recog- nition. In European Conference on Computer Vision, pages 303-321. Springer, 2022.","title":"Toward understanding wordart: Corner-guided transformer for scene text recognition","authors":"Xudong Xie, Ling Fu, Zhifei Zhang, Zhaowen Wang, Xiang Bai","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Yang et al., 2022] Mingkun Yang, Minghui Liao, Pu Lu, Jing Wang, Shenggao Zhu, Hualin Luo, Qi Tian, and Xi- ang Bai. Reading and writing: Discriminative and gen- erative modeling for self-supervised text recognition. In Proceedings of the 30th ACM International Conference on Multimedia, pages 4214-4223, 2022.","title":"Reading and writing: Discriminative and generative modeling for self-supervised text recognition","authors":"Mingkun Yang, Minghui Liao, Pu Lu, Jing Wang, Shenggao Zhu, Hualin Luo, Qi Tian, Xiang Bai","year":"2022","venue":"Proceedings of the 30th ACM International Conference on Multimedia","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":"[Yue et al., 2020] Xiaoyu Yue, Zhanghui Kuang, Chenhao Lin, Hongbin Sun, and Wayne Zhang. Robustscanner: Dynamically enhancing positional clues for robust text recognition. In European Conference on Computer Vision, pages 135-151. Springer, 2020.","title":"Robustscanner: Dynamically enhancing positional clues for robust text recognition","authors":"Xiaoyu Yue, Zhanghui Kuang, Chenhao Lin, Hongbin Sun, Wayne Zhang","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zhang et al., 2017] Ying Zhang, Lionel Gueguen, Ilya Zharkov, Peter Zhang, Keith Seifert, and Ben Kadlec. Uber-text: A large-scale dataset for optical character recognition from street-level imagery. In SUNw: Scene Understanding Workshop-CVPR, volume 2017, page 5, 2017.","title":"Uber-text: A large-scale dataset for optical character recognition from street-level imagery","authors":"Ying Zhang, Lionel Gueguen, Ilya Zharkov, Peter Zhang, Keith Seifert, Ben Kadlec","year":"2017","venue":"SUNw: Scene Understanding Workshop-CVPR","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zhang et al., 2022] Xinyun Zhang, Binwu Zhu, Xufeng Yao, Qi Sun, Ruiyu Li, and Bei Yu. Context-based con- trastive learning for scene text recognition. In Proceed- ings of the AAAI Conference on Artificial Intelligence, vol- ume 36, pages 3353-3361, 2022.","title":"Context-based contrastive learning for scene text recognition","authors":"Xinyun Zhang, Binwu Zhu, Xufeng Yao, Qi Sun, Ruiyu Li, Bei Yu","year":"2022","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhang et al., 2023] Boqiang Zhang, Hongtao Xie, Yuxin Wang, Jianjun Xu, and Yongdong Zhang. Linguistic more: Taking a further step toward efficient and accurate scene text recognition. arXiv preprint arXiv:2305.05140, 2023.","title":"Linguistic more: Taking a further step toward efficient and accurate scene text recognition","authors":"Boqiang Zhang, Hongtao Xie, Yuxin Wang, Jianjun Xu, Yongdong Zhang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2023/189"}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}