{"type":"ScholarlyArticle","title":"Divide Rows and Conquer Cells: Towards Structure Recognition for Large Tables","authors":[{"name":"Huawen Shen","slug":"huawen-shen","given":"Huawen","family":"Shen","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences"},{"name":"Xiang Gao","slug":"xiang-gao","given":"Xiang","family":"Gao","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences"},{"name":"Jin Wei","slug":"jin-wei","given":"Jin","family":"Wei","affiliation":"School of Information and Communication Engineering , Communication University of China; Institute of Information Engineering , Chinese Academy of Sciences"},{"name":"Liang Qiao","slug":"liang-qiao","given":"Liang","family":"Qiao","affiliation":"Hikvision Research Institute , China"},{"name":"Yu Zhou","slug":"yu-zhou","given":"Yu","family":"Zhou","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences"},{"name":"Qiang Li","slug":"qiang-li","given":"Qiang","family":"Li","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences"},{"name":"Zhanzhan Cheng","slug":"zhanzhan-cheng","given":"Zhanzhan","family":"Cheng","affiliation":"Hikvision Research Institute , China"}],"abstract":"Recent advanced Table Structure Recognition (TSR) models adopt image-to-text solutions to parse table structure. These methods can be formulated as image caption problem, i.e., input a single-table image and output table structure description in a specific text format, e.g., HTML. With the impressive success of Transformer in text generation tasks, these methods use Transformer architecture to predict HTML table text in an autoregressive manner. However, tables always emerge with a large variety of shapes and sizes. Autoregressive models usually suffer from the error accumulation problem as the length of predicted text increases, which results in unsatisfactory performance for large tables. In this paper, we propose a novel image-to-text based TSR method that relieves error accumulation problems and improves performance noticeably. At the core of our method is a cascaded two-step decoder architecture with the former decoder predicting HTML table row tags non-autoregressively and the latter predicting HTML table cell tags of each row in a semi-autoregressive manner. Compared with existing methods that predict HTML text autoregressively, the superiority of our row-to-cell progressive table parsing is twofold: (1) it generates an HTML tag sequence with a vertical-and-horizontal two-step `scanning', which better fits the inherent 2D structure of image data, (2) it performs substantially better for large tables (long sequence prediction) since it alleviates error accumulation problem specific to autoregressive models. Extensive experiments demonstrate that our method achieves competitive performance on three public benchmarks.","doi":"10.24963/ijcai.2023/152","url":"https://ijcai.org/proceedings/2023/152","pdf":"https://ijcai.org/proceedings/2023/0152.pdf","markdown":"https://ijcai.org/proceedings/2023/152.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/152","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":152,"pages":{"first":1369,"last":1377},"keywords":["Computer Vision: CV: Applications","Computer Vision: CV: Recognition (object detection, categorization)"],"videos":[],"references":[{"position":1,"raw":"[Akoury et al., 2019] Nader Akoury, Kalpesh Krishna, and Mohit Iyyer. Syntactically supervised transformers for faster neural machine translation. arXiv preprint arXiv:1906.02780, 2019.","title":"Syntactically supervised transformers for faster neural machine translation","authors":"Nader Akoury, Kalpesh Krishna, Mohit Iyyer","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Carion et al., 2020] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European Conference on Computer Vision, pages 213-229. Springer, 2020.","title":"End-to-end object detection with transformers","authors":"Carion","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chi et al., 2019] Zewen Chi, Heyan Huang, Heng-Da Xu, Houjin Yu, Wanxuan Yin, and Xian-Ling Mao. Com- plicated table structure recognition. arXiv preprint arXiv:1908.04729, 2019.","title":"Complicated table structure recognition","authors":"Chi","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Deng et al., 2017] Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, and Alexander M Rush. Image-to-markup genera- tion with coarse-to-fine attention. In International Confer- ence on Machine Learning, pages 980-989. PMLR, 2017.","title":"Image-to-markup generation with coarse-to-fine attention","authors":"Deng","year":"2017","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Deng et al., 2019a] Yuntian Deng, David Rosenberg, and Gideon Mann. Challenges in end-to-end neural scientific table recognition. In International Conference on Doc- ument Analysis and Recognition, pages 894-901. IEEE, 2019.","title":"Challenges in end-to-end neural scientific table recognition","authors":"Deng","year":"2019","venue":"International Conference on Document Analysis and Recognition","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Deng et al., 2019b] Yuntian Deng, David Rosenberg, and Gideon Mann. Challenges in end-to-end neural scientific table recognition. In International Conference on Doc- ument Analysis and Recognition, pages 894-901. IEEE, 2019.","title":"Challenges in end-to-end neural scientific table recognition","authors":"Deng","year":"2019","venue":"International Conference on Document Analysis and Recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Dosovitskiy et al., 2020] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Min- derer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.","title":"An image is worth 16x16 words: Transformers for image recognition at scale","authors":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Goldberg and Elhadad, 2010] Yoav Goldberg and Michael Elhadad. An efficient algorithm for easy-first non- directional dependency parsing. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 742-750, 2010.","title":"An efficient algorithm for easy-first nondirectional dependency parsing","authors":"Yoav Goldberg, Michael Elhadad","year":"2010","venue":"Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. 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":10,"raw":"[He et al., 2017] Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask R-CNN. In Proceed- ings of the IEEE International Conference on Computer Vision, pages 2961-2969, 2017.","title":"Mask R-CNN","authors":"He","year":"2017","venue":"Proceedings of the IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Itonori, 1993] Katsuhiko Itonori. Table structure recogni- tion based on textblock arrangement and ruled line posi- tion. In International Conference on Document Analysis and Recognition, pages 765-768. IEEE, 1993.","title":"Table structure recognition based on textblock arrangement and ruled line position","authors":"Katsuhiko Itonori","year":"1993","venue":"International Conference on Document Analysis and Recognition","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Jimeno Yepes et al., 2021] Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. ICDAR 2021 competition on scientific literature parsing. In International Confer- ence on Document Analysis and Recognition, pages 605- 617. Springer, 2021.","title":"International Conference on Document Analysis and Recognition","authors":"Jimeno Yepes","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li et al., 2020] Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, and Zhoujun Li. TableBank: Table benchmark for image-based table detection and recogni- tion. In Language Resources and Evaluation Conference, pages 1918-1925, 2020.","title":"TableBank: Table benchmark for image-based table detection and recognition","authors":"Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, Zhoujun Li","year":"2020","venue":"Language Resources and Evaluation Conference","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Lin et al., 2022] Weihong Lin, Zheng Sun, Chixiang Ma, Mingze Li, Jiawei Wang, Lei Sun, and Qiang Huo. TSR- Former: Table structure recognition with transformers. In ACM International Conference on Multimedia, pages 6473-6482, 2022.","title":"TSR-Former: Table structure recognition with transformers","authors":"Weihong Lin, Zheng Sun, Chixiang Ma, Mingze Li, Jiawei Wang, Lei Sun, Qiang Huo","year":"2022","venue":"ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu et al., 2021] Hao Liu, Xin Li, Bing Liu, Deqiang Jiang, Yinsong Liu, Bo Ren, and Rongrong Ji. Show, read and reason: Table structure recognition with flexible context aggregator. In ACM International Conference on Multi- media, pages 1084-1092, 2021.","title":"Show, read and reason: Table structure recognition with flexible context aggregator","authors":"Hao Liu, Xin Li, Bing Liu, Deqiang Jiang, Yinsong Liu, Bo Ren, Rongrong Ji","year":"2021","venue":"ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Long et al., 2021] Rujiao Long, Wen Wang, Nan Xue, Feiyu Gao, Zhibo Yang, Yongpan Wang, and Gui-Song Xia. Parsing table structures in the wild. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 944-952, 2021.","title":"Parsing table structures in the wild","authors":"Rujiao Long, Wen Wang, Nan Xue, Feiyu Gao, Zhibo Yang, Yongpan Wang, Gui-Song Xia","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Ma et al., 2023] Chixiang Ma, Weihong Lin, Lei Sun, and Qiang Huo. Robust table detection and structure recogni- tion from heterogeneous document images. Pattern Recog- nition, 133:109006, 2023.","title":"Robust table detection and structure recognition from heterogeneous document images","authors":"Ma","year":"2023","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Nassar et al., 2022] Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, and Peter Staar. TableFormer: Table structure understanding with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 4614-4623, 2022.","title":"TableFormer: Table structure understanding with transformers","authors":"Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Prasad et al., 2020] Devashish Prasad, Ayan Gadpal, Kshi- tij Kapadni, Manish Visave, and Kavita Sultanpure. Cas- cadeTabNet: An approach for end to end table detection and structure recognition from image-based documents. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 572- 573, 2020.","title":"Cas-cadeTabNet: An approach for end to end table detection and structure recognition from image-based documents","authors":"Devashish Prasad, Ayan Gadpal, Kshitij Kapadni, Manish Visave, Kavita Sultanpure","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops","doi":null,"url":null,"internal_url":null},{"position":20,"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":21,"raw":"[Qiao et al., 2021a] Liang Qiao, Zaisheng Li, Zhanzhan Cheng, Peng Zhang, Shiliang Pu, Yi Niu, Wenqi Ren, Wenming Tan, and Fei Wu. LGPMA: Complicated ta- ble structure recognition with local and global pyramid mask alignment. In International Conference on Docu- ment Analysis and Recognition, pages 99-114. Springer, 2021.","title":"LGPMA: Complicated table structure recognition with local and global pyramid mask alignment","authors":"Qiao","year":"2021","venue":"International Conference on Document Analysis and Recognition","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Qiao et al., 2021b] Zhi Qiao, Yu Zhou, Jin Wei, Wei Wang, Yuan Zhang, Ning Jiang, Hongbin Wang, and Weiping Wang. PIMNet: A parallel, iterative and mimicking net- work for scene text recognition. In ACM International Conference on Multimedia, pages 2046-2055, 2021.","title":"PIMNet: A parallel, iterative and mimicking network for scene text recognition","authors":"Qiao","year":"2021","venue":"ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Qin et al., 2021] Xugong Qin, Yu Zhou, Youhui Guo, Dayan Wu, Zhihong Tian, Ning Jiang, Hongbin Wang, and Weiping Wang. Mask is all you need: Rethinking mask R- CNN for dense and arbitrary-shaped scene text detection. In ACM International Conference on Multimedia, pages 414-423, 2021.","title":"Mask is all you need: Rethinking mask R-CNN for dense and arbitrary-shaped scene text detection","authors":"Xugong Qin, Yu Zhou, Youhui Guo, Dayan Wu, Zhihong Tian, Ning Jiang, Hongbin Wang, Weiping Wang","year":"2021","venue":"ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Raja et al., 2020] Sachin Raja, Ajoy Mondal, and CV Jawa- har. Table structure recognition using top-down and bottom-up cues. In European Conference on Computer Vision, pages 70-86. Springer, 2020.","title":"Table structure recognition using top-down and bottom-up cues","authors":"Sachin Raja, Ajoy Mondal, Jawahar","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Raja et al., 2022] Sachin Raja, Ajoy Mondal, and CV Jawa- har. Visual understanding of complex table structures from document images. In Proceedings of the IEEE/CVF Win- ter Conference on Applications of Computer Vision, pages 2299-2308, 2022.","title":"Visual understanding of complex table structures from document images","authors":"Sachin Raja, Ajoy Mondal, Jawahar","year":"2022","venue":"Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Redmon et al., 2016] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Uni- fied, real-time object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 779-788, 2016.","title":"You only look once: Unified, real-time object detection","authors":"Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi","year":"2016","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Ren et al., 2015] Shaoqing Ren, Kaiming He, Ross Gir- shick, and Jian Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 28, 2015.","title":"Faster R-CNN: Towards real-time object detection with region proposal networks","authors":"Ren","year":"2015","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Schreiber et al., 2017] Sebastian Schreiber, Stefan Agne, Ivo Wolf, Andreas Dengel, and Sheraz Ahmed. Deep- DeSRT: Deep learning for detection and structure recogni- tion of tables in document images. In International Con- ference on Document Analysis and Recognition, volume 1, pages 1162-1167. IEEE, 2017.","title":"Deep-DeSRT: Deep learning for detection and structure recognition of tables in document images","authors":"Sebastian Schreiber, Stefan Agne, Ivo Wolf, Andreas Dengel, Sheraz Ahmed","year":"2017","venue":"International Conference on Document Analysis and Recognition","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Siddiqui et al., 2019] Shoaib Ahmed Siddiqui, Per- vaiz Iqbal Khan, Andreas Dengel, and Sheraz Ahmed. Rethinking semantic segmentation for table structure recognition in documents. In International Conference on Document Analysis and Recognition, pages 1397-1402. IEEE, 2019.","title":"Rethinking semantic segmentation for table structure recognition in documents","authors":"Ahmed Shoaib, Pervaiz Siddiqui, Andreas Iqbal Khan, Sheraz Dengel, Ahmed","year":"2019","venue":"International Conference on Document Analysis and Recognition","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Tensmeyer et al., 2019] Chris Tensmeyer, Vlad I Morariu, Brian Price, Scott Cohen, and Tony Martinez. Deep split- ting and merging for table structure decomposition. In In- ternational Conference on Document Analysis and Recog- nition, pages 114-121. IEEE, 2019.","title":"Deep splitting and merging for table structure decomposition","authors":"Chris Tensmeyer, Brian Vlad I Morariu, Scott Price, Tony Cohen, Martinez","year":"2019","venue":"International Conference on Document Analysis and Recognition","doi":null,"url":null,"internal_url":null},{"position":31,"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 Sys- tems, 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":32,"raw":"[Wang et al., 2022] Wei Wang, Yu Zhou, Jiahao Lv, Dayan Wu, Guoqing Zhao, Ning Jiang, and Weipinng Wang. TP- SNet: Reverse thinking of thin plate splines for arbitrary shape scene text representation. In ACM International Conference on Multimedia, pages 5014-5025, 2022.","title":"TP-SNet: Reverse thinking of thin plate splines for arbitrary shape scene text representation","authors":"Wei Wang, Yu Zhou, Jiahao Lv, Dayan Wu, Guoqing Zhao, Ning Jiang, Weipinng Wang","year":"2022","venue":"ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wei et al., 2022] Jin Wei, Yuan Zhang, Yu Zhou, Gangyan Zeng, Zhi Qiao, Youhui Guo, Haiying Wu, Hongbin Wang, and Weipinng Wang. TextBlock: Towards scene text spotting without fine-grained detection. In ACM In- ternational Conference on Multimedia, pages 5892-5902, 2022.","title":"TextBlock: Towards scene text spotting without fine-grained detection","authors":"Jin Wei, Yuan Zhang, Yu Zhou, Gangyan Zeng, Zhi Qiao, Youhui Guo, Haiying Wu, Hongbin Wang, Weipinng Wang","year":"2022","venue":"ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Xu et al., 2015] Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. Show, attend and tell: Neural image caption generation with visual attention. In International Conference on Machine Learning, pages 2048-2057. PMLR, 2015.","title":"Show, attend and tell: Neural image caption generation with visual attention","authors":"Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, Yoshua Bengio","year":"2015","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Xu et al., 2020] Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, et al. LayoutLMv2: Multi- modal pre-training for visually-rich document understand- ing. arXiv preprint arXiv:2012.14740, 2020.","title":"LayoutLMv2: Multimodal pre-training for visually-rich document understanding","authors":"Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Xue et al., 2021] Wenyuan Xue, Baosheng Yu, Wen Wang, Dacheng Tao, and Qingyong Li. TGRNet: A table graph reconstruction network for table structure recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1295-1304, 2021.","title":"TGRNet: A table graph reconstruction network for table structure recognition","authors":"Wenyuan Xue, Baosheng Yu, Wen Wang, Dacheng Tao, Qingyong Li","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Ye et al., 2021] Jiaquan Ye, Xianbiao Qi, Yelin He, Yihao Chen, Dengyi Gu, Peng Gao, and Rong Xiao. PingAn- VCGroup's solution for ICDAR 2021 competition on scientific literature parsing task B: Table recognition to HTML. arXiv preprint arXiv:2105.01848, 2021.","title":"PingAn-VCGroup's solution for ICDAR 2021 competition on scientific literature parsing task B: Table recognition to HTML","authors":"Ye","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zeng et al., 2023] Gangyan Zeng, Yuan Zhang, Yu Zhou, Xiaomeng Yang, Ning Jiang, Guoqing Zhao, Weiping Wang, and Xu-Cheng Yin. Beyond OCR+ VQA: Towards end-to-end reading and reasoning for robust and accurate TextVQA. Pattern Recognition, 138:109337, 2023.","title":"Beyond OCR+ VQA: Towards end-to-end reading and reasoning for robust and accurate TextVQA","authors":"Gangyan Zeng, Yuan Zhang, Yu Zhou, Xiaomeng Yang, Ning Jiang, Guoqing Zhao, Weiping Wang, Xu-Cheng Yin","year":"2023","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhong et al., 2020] Xu Zhong, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. Image-based table recognition: data, model, and evaluation. In European Conference on Computer Vision, pages 564-580. Springer, 2020.","title":"Image-based table recognition: data, model, and evaluation","authors":"Xu Zhong, Elaheh Shafieibavani, Antonio Jimeno, Yepes","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}