{"type":"ScholarlyArticle","title":"SEMv3: A Fast and Robust Approach to Table Separation Line Detection","authors":[{"name":"Chunxia Qin","slug":"chunxia-qin","given":"Chunxia","family":"Qin","affiliation":"University of Science and Technology of China"},{"name":"Zhenrong Zhang","slug":"zhenrong-zhang","given":"Zhenrong","family":"Zhang","affiliation":"University of Science and Technology of China"},{"name":"Pengfei Hu","slug":"pengfei-hu","given":"Pengfei","family":"Hu","affiliation":"University of Science and Technology of China"},{"name":"Chenyu Liu","slug":"chenyu-liu","given":"Chenyu","family":"Liu","affiliation":"iFLYTEK Research"},{"name":"Jiefeng Ma","slug":"jiefeng-ma","given":"Jiefeng","family":"Ma","affiliation":"University of Science and Technology of China"},{"name":"Jun Du","slug":"jun-du","given":"Jun","family":"Du","affiliation":"University of Science and Technology of China"}],"abstract":"Table structure recognition (TSR) aims to parse the inherent structure of a table from its input image. The \"split-and-merge\" paradigm is a pivotal approach to parse table structure, where the table separation line detection is crucial. However, challenges such as wireless and deformed tables make it demanding. In this paper, we adhere to the \"split-and-merge\" paradigm and propose SEMv3 (SEM: Split, Embed and Merge), a method that is both fast and robust for detecting table separation lines. During the split stage, we introduce a Keypoint Offset Regression (KOR) module, which effectively detects table separation lines by directly regressing the offset of each line relative to its keypoint proposals. Moreover, in the merge stage, we define a series of merge actions to efficiently describe the table structure based on table grids. Extensive ablation studies demonstrate that our proposed KOR module can detect table separation lines quickly and accurately. Furthermore, on public datasets (e.g. WTW, ICDAR-2019 cTDaR Historical and iFLYTAB), SEMv3 achieves state-of-the-art (SOTA) performance. The code is available at https://github.com/Chunchunwumu/SEMv3.","doi":"10.24963/ijcai.2024/132","url":"https://ijcai.org/proceedings/2024/132","pdf":"https://ijcai.org/proceedings/2024/0132.pdf","markdown":"https://ijcai.org/proceedings/2024/132.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/132","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":132,"pages":{"first":1191,"last":1199},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Applications"],"videos":[],"references":[{"position":1,"raw":"[Baek et al., 2023] Youngmin Baek, Daehyun Nam, Jaehe- ung Surh, Seung Shin, and Seonghyeon Kim. Trace: Table reconstruction aligned to corner and edges. In Document Analysis and Recognition -ICDAR 2023: 17th Interna- tional Conference, page 472-489, 2023.","title":"Trace: Table reconstruction aligned to corner and edges","authors":"Youngmin Baek, Daehyun Nam, Jaeheung Surh, Seung Shin, Seonghyeon Kim","year":"2023","venue":"Document Analysis and Recognition -ICDAR 2023: 17th International Conference","doi":null,"url":null,"internal_url":null},{"position":2,"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":3,"raw":"[Cho et al., 2014] Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. On the proper- ties of neural machine translation: Encoder-decoder ap- proaches. In Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pages 103-111, October 2014.","title":"On the properties of neural machine translation: Encoder-decoder approaches","authors":"Cho","year":"2014","venue":"Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gao et al., 2019] Liangcai Gao, Yilun Huang, Hervé Déjean, Jean-Luc Meunier, Qinqin Yan, Yu Fang, Florian Kleber, and Eva Lang. Icdar 2019 competition on table detection and recognition (ctdar). In 2019 International Conference on Document Analysis and Recognition (IC- DAR), pages 1510-1515, 2019.","title":"Icdar 2019 competition on table detection and recognition (ctdar)","authors":"Gao","year":"2019","venue":"2019 International Conference on Document Analysis and Recognition (IC-DAR)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Göbel et al., 2012] Max Göbel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. A methodology for evaluating algorithms for table understanding in pdf documents. In Proceedings of the 2012 ACM Symposium on Document Engineering, page 45-48, 2012.","title":"A methodology for evaluating algorithms for table understanding in pdf documents","authors":"Göbel","year":"2012","venue":"Proceedings of the 2012 ACM Symposium on Document Engineering","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[He et al., 2017] Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask r-cnn. In 2017 IEEE In- ternational Conference on Computer Vision (ICCV), pages 2980-2988, 2017.","title":"Mask r-cnn","authors":"He","year":"2017","venue":"IEEE International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Huang et al., 2023] Yongshuai Huang, Ning Lu, Dapeng Chen, Yibo Li, Zecheng Xie, Shenggao Zhu, Liangcai Gao, and Wei Peng. Improving table structure recog- nition with visual-alignment sequential coordinate mod- eling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11134-11143, June 2023.","title":"Improving table structure recognition with visual-alignment sequential coordinate modeling","authors":"Yongshuai Huang, Ning Lu, Dapeng Chen, Yibo Li, Zecheng Xie, Shenggao Zhu, Liangcai Gao, Wei Peng","year":"2023","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kingma and Ba, 2014] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. Computer Science, 2014. [Krizhevsky et al., 2012]","title":"Adam: A method for stochastic optimization","authors":"Diederik Kingma, Jimmy Ba","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"Alex Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolu- tional neural networks. Advances in neural information processing systems, 25(2), 2012.","title":"Imagenet classification with deep convolutional neural networks","authors":"Alex Krizhevsky, I Sutskever, G Hinton","year":"2012","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lin et al., 2017a] Tsung-Yi Lin, Piotr Dollár, Ross Gir- shick, Kaiming He, Bharath Hariharan, and Serge Be- longie. Feature pyramid networks for object detection. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 936-944, 2017.","title":"Feature pyramid networks for object detection","authors":"Lin","year":"2017","venue":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Lin et al., 2017b] Tsung-Yi Lin, Priya Goyal, Ross Gir- shick, Kaiming He, and Piotr Dollár. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980-2988, 2017.","title":"Focal loss for dense object detection","authors":"Lin","year":"2017","venue":"Proceedings of the IEEE international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":13,"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 Proceedings of the 30th ACM International Conference on Multimedia, pages 6473-6482, 2022.","title":"Tsrformer: Table structure recognition with transformers","authors":"Weihong Lin, Zheng Sun, Chixiang Ma, Mingze Li, Jiawei Wang, Lei Sun, Qiang Huo","year":"2022","venue":"Proceedings of the 30th ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":14,"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 Proceedings of the 29th ACM International Conference on Multimedia, 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":"Proceedings of the 29th ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu et al., 2022] Hao Liu, Xin Li, Bing Liu, Deqiang Jiang, Yinsong Liu, and Bo Ren. Neural collaborative graph ma- chines for table structure recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 4533-4542, 2022.","title":"Neural collaborative graph machines for table structure recognition","authors":"Hao Liu, Xin Li, Bing Liu, Deqiang Jiang, Yinsong Liu, Bo Ren","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","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 (ICCV), pages 944-952, October 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 (ICCV)","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Loshchilov and Hutter, 2016] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016.","title":"Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts","authors":"Hutter Loshchilov","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Lysak et al., 2023] Maksym Lysak, Ahmed Nassar, Niko- laos Livathinos, Christoph Auer, and Peter Staar. Opti- mized table tokenization for table structure recognition. In Document Analysis and Recognition -ICDAR 2023: 17th International Conference, page 37-50, 2023.","title":"Optimized table tokenization for table structure recognition","authors":"Maksym Lysak, Ahmed Nassar, Nikolaos Livathinos, Christoph Auer, Peter Staar","year":"2023","venue":"Document Analysis and Recognition -ICDAR 2023: 17th International Conference","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lyu et al., 2023] Pengyuan Lyu, Weihong Ma, Hongyi Wang, Yuechen Yu, Chengquan Zhang, Kun Yao, Yang Xue, and Jingdong Wang. Gridformer: Towards accurate table structure recognition via grid prediction. In Proceed- ings of the 31st ACM International Conference on Multi- media, page 7747-7757, 2023.","title":"Gridformer: Towards accurate table structure recognition via grid prediction","authors":"Lyu","year":"2023","venue":"Proceedings of the 31st ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":20,"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":21,"raw":"[Nassar et al., 2022] Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, and Peter Staar. Tableformer: Table struc- ture understanding with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), pages 4614-4623, June 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 (CVPR)","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Nguyen et al., 2023] Nam Quan Nguyen, Anh Duy Le, Anh Khoa Lu, Xuan Toan Mai, and Tuan Anh Tran. For- merge: Recover spanning cells in complex table structure using transformer network. In International Conference on Document Analysis and Recognition, pages 522-534, 2023.","title":"Formerge: Recover spanning cells in complex table structure using transformer network","authors":"Nguyen","year":"2023","venue":"International Conference on Document Analysis and Recognition","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Pan et al., 2018] Xingang Pan, Jianping Shi, Ping Luo, Xi- aogang Wang, and Xiaoou Tang. Spatial as deep: Spatial cnn for traffic scene understanding. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelli- gence Conference and Eighth AAAI Symposium on Educa- tional Advances in Artificial Intelligence, 2018.","title":"Spatial as deep: Spatial cnn for traffic scene understanding","authors":"Xingang Pan, Jianping Shi, Ping Luo, Xiaogang Wang, Xiaoou Tang","year":"2018","venue":"Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":24,"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 vi- sion and pattern recognition workshops, pages 572-573, 2020.","title":"Cascadetabnet: 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":25,"raw":"[Qiao et al., 2021] Liang Qiao, Zaisheng Li, Zhanzhan Cheng, Peng Zhang, Shiliang Pu, Yi Niu, Wenqi Ren, Wenming Tan, and Fei Wu. Lgpma: Complicated table structure recognition with local and global pyramid mask alignment. In International conference on document anal- ysis and recognition, pages 99-114, 2021.","title":"Lgpma: Complicated table structure recognition with local and global pyramid mask alignment","authors":"Liang Qiao, Zaisheng Li, Zhanzhan Cheng, Peng Zhang, Shiliang Pu, Yi Niu, Wenqi Ren, Wenming Tan, Fei Wu","year":"2021","venue":"International conference on document analysis and recognition","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Raja et al., 2020] Sachin Raja, Ajoy Mondal, and C. V. Jawahar. Table structure recognition using top-down and bottom-up cues. In Computer Vision -ECCV 2020: 16th European Conference, page 70-86, 2020.","title":"Table structure recognition using top-down and bottom-up cues","authors":"Sachin Raja, Ajoy Mondal, C V Jawahar","year":"2020","venue":"Computer Vision -ECCV 2020: 16th European Conference","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Schreiber et al., 2017] Sebastian Schreiber, Stefan Agne, Ivo Wolf, Andreas Dengel, and Sheraz Ahmed. Deepdesrt: Deep learning for detection and structure recognition of tables in document images. In 2017 14th IAPR Interna- tional Conference on Document Analysis and Recognition (ICDAR), 2017.","title":"Deepdesrt: 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":"14th IAPR International Conference on Document Analysis and Recognition (ICDAR)","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Shen et al., 2023] Huawen Shen, Xiang Gao, Jin Wei, Liang Qiao, Yu Zhou, Qiang Li, and Zhanzhan Cheng. Divide rows and conquer cells: Towards structure recognition for large tables. In Proceedings of the Thirty-Second Inter- national Joint Conference on Artificial Intelligence, pages 1369-1377, 2023.","title":"Divide rows and conquer cells: Towards structure recognition for large tables","authors":"Huawen Shen, Xiang Gao, Jin Wei, Liang Qiao, Yu Zhou, Qiang Li, Zhanzhan Cheng","year":"2023","venue":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2023/152"},{"position":29,"raw":"[Smock et al., 2022] Brandon Smock, Rohith Pesala, and Robin Abraham. Pubtables-1m: Towards comprehensive table extraction from unstructured documents. In Pro- ceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition (CVPR), pages 4634-4642, June 2022.","title":"Pubtables-1m: Towards comprehensive table extraction from unstructured documents","authors":"Brandon Smock, Rohith Pesala, Robin Abraham","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","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 2019 International Conference on Document Analysis and Recognition (ICDAR), pages 114-121, 2019.","title":"Deep splitting and merging for table structure decomposition","authors":"Chris Tensmeyer, Vlad I Morariu, Brian Price, Scott Cohen, Tony Martinez","year":"2019","venue":"2019 International Conference on Document Analysis and Recognition (ICDAR)","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wang et al., 2023] Jiawei Wang, Weihong Lin, Chixiang Ma, Mingze Li, Zheng Sun, Lei Sun, and Qiang Huo. Robust table structure recognition with dynamic queries enhanced detection transformer. Pattern Recognition, 144:109817, 2023.","title":"Robust table structure recognition with dynamic queries enhanced detection transformer","authors":"Jiawei Wang, Weihong Lin, Chixiang Ma, Mingze Li, Zheng Sun, Lei Sun, Qiang Huo","year":"2023","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Xiao et al., 2022] Bin Xiao, Murat Simsek, Burak Kantarci, and Ala Abu Alkheir. Table structure recognition with con- ditional attention. arXiv preprint arXiv:2203.03819, 2022.","title":"Table structure recognition with conditional attention","authors":"Xiao","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Xing et al., 2023] Hangdi Xing, Feiyu Gao, Rujiao Long, Jiajun Bu, Qi Zheng, Liangcheng Li, Cong Yao, and Zhi Yu. Lore: Logical location regression network for ta- ble structure recognition. In Proceedings of the Thirty- Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Ar- tificial Intelligence and Thirteenth Symposium on Educa- tional Advances in Artificial Intelligence, 2023.","title":"Lore: Logical location regression network for table structure recognition","authors":"Xing","year":"2023","venue":"Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Xu et al., 2020] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Con- ference on Knowledge Discovery & Data Mining, pages 1192-1200, 2020.","title":"Layoutlm: Pre-training of text and layout for document image understanding","authors":"Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou","year":"2020","venue":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Xue et al., 2019] Wenyuan Xue, Qingyong Li, and Dacheng Tao. Res2tim: Reconstruct syntactic structures from ta- ble images. In 2019 international conference on document analysis and recognition (ICDAR), pages 749-755, 2019.","title":"Res2tim: Reconstruct syntactic structures from table images","authors":"Wenyuan Xue, Qingyong Li, Dacheng Tao","year":"2019","venue":"2019 international conference on document analysis and recognition (ICDAR)","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zhang et al., 2017] Ji Zhang, Mohamed Elhoseiny, Scott Cohen, Walter Chang, and Ahmed Elgammal. Relation- ship proposal networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5226-5234, 2017.","title":"Relationship proposal networks","authors":"Ji Zhang, Mohamed Elhoseiny, Scott Cohen, Walter Chang, Ahmed Elgammal","year":"2017","venue":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zhang et al., 2022] Zhenrong Zhang, Jianshu Zhang, Jun Du, and Fengren Wang. Split, embed and merge: An accurate table structure recognizer. Pattern Recognition, 126:108565, 2022.","title":"Split, embed and merge: An accurate table structure recognizer","authors":"Zhenrong Zhang, Jianshu Zhang, Jun Du, Fengren Wang","year":"2022","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zhang et al., 2024] Zhenrong Zhang, Pengfei Hu, Jiefeng Ma, Jun Du, Jianshu Zhang, Baocai Yin, Bing Yin, and Cong Liu. Semv2: Table separation line detection based on instance segmentation. Pattern Recognition, page 110279, 2024.","title":"Semv2: Table separation line detection based on instance segmentation","authors":"Zhenrong Zhang, Pengfei Hu, Jiefeng Ma, Jun Du, Jianshu Zhang, Baocai Yin, Bing Yin, Cong Liu","year":"2024","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zheng et al., 2021] Xinyi Zheng, Douglas Burdick, Lucian Popa, Xu Zhong, and Nancy Xin Ru Wang. Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In Pro- ceedings of the IEEE/CVF Winter Conference on Appli- cations of Computer Vision (WACV), pages 697-706, Jan- uary 2021.","title":"Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context","authors":"Xinyi Zheng, Douglas Burdick, Lucian Popa, Xu Zhong, Nancy Xin, Ru Wang","year":"2021","venue":"Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","doi":null,"url":null,"internal_url":null},{"position":40,"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, 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},{"position":41,"raw":"[Zou and Ma, 2020] Yajun Zou and Jinwen Ma. A deep se- mantic segmentation model for image-based table struc- ture recognition. In 2020 15th IEEE International Confer- ence on Signal Processing (ICSP), volume 1, pages 274- 280, 2020.","title":"A deep semantic segmentation model for image-based table structure recognition","authors":"Ma ; ] Yajun Zou, Jinwen Zou, Ma","year":"2020","venue":"15th IEEE International Conference on Signal Processing (ICSP)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}