{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T08:19:27Z","timestamp":1768983567630,"version":"3.49.0"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032046260","type":"print"},{"value":"9783032046277","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04627-7_8","type":"book-chapter","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T02:06:45Z","timestamp":1757988405000},"page":"137-153","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SepFormer: Coarse-to-Fine Separator Regression Network for\u00a0Table Structure Recognition"],"prefix":"10.1007","author":[{"given":"Nam Quan","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan Phong","family":"Pham","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuan-Anh","family":"Tran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Tensmeyer, C., Morariu, V.I., Price, B.L., Cohen, S., Martinez, T.R.: Deep splitting and merging for table structure decomposition. In: 2019 International Conference on Document Analysis and Recognition, ICDAR 2019, pp. 114\u2013121 (2019)","DOI":"10.1109\/ICDAR.2019.00027"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S: End-to-end object detection with transformers. In: European Conference on Computer Vision, pp. 213\u2013229 (2020)","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"8_CR3","unstructured":"Zhang, H., et al.: Dino: DETR with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605 (2022)"},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Zhao, Y., et al.: DETRs beat YOLOs on real-time object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16965\u201316974 (2024)","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Khan, S.A., Khalid, S.M.D., Shahzad, M.A., Shafait, F.: Table structure extraction with bi-directional gated recurrent unit networks. In: 2019 International Conference on Document Analysis and Recognition, ICDAR 2019, pp. 1366\u20131371 (2019)","DOI":"10.1109\/ICDAR.2019.00220"},{"issue":"7","key":"8_CR7","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1016\/j.patcog.2004.01.012","volume":"37","author":"Y Wang","year":"2004","unstructured":"Wang, Y., Phillips, I.T., Haralick, R.M.: Table structure understanding and its performance evaluation. Pattern Recognit. 37(7), 1479\u20131497 (2004)","journal-title":"Pattern Recognit."},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Chen, J., Lopresti, D.: Model-based tabular structure detection and recognition in noisy handwritten documents. In: 2012 International Conference on Frontiers in Handwriting Recognition, pp. 75\u201380 (2012)","DOI":"10.1109\/ICFHR.2012.233"},{"key":"8_CR9","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.jvcir.2016.05.023","volume":"39","author":"TA Tran","year":"2016","unstructured":"Tran, T.A., Tran, H.T., Na, I.S., Lee, G.S., Yang, H.J., Kim, S.H.: A mixture model using random rotation bounding box to detect table region in document image. J. Vis. Commun. Image Represent. 39, 196\u2013208 (2016)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"8_CR10","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. In: International Conference on Learning Representations (2020)"},{"key":"8_CR11","unstructured":"Li, M., Cui, L., Huang, S., Wei, F., Zhou, M., Li, Z.: Tablebank: table benchmark for image-based table detection and recognition. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 1918\u20131925 (2020)"},{"key":"8_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1007\/978-3-030-58589-1_34","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Zhong","year":"2020","unstructured":"Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12366, pp. 564\u2013580. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58589-1_34"},{"key":"8_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/978-3-030-86331-9_35","volume-title":"Document Analysis and Recognition \u2013 ICDAR 2021","author":"Y Li","year":"2021","unstructured":"Li, Y., et al.: Rethinking table structure recognition using sequence labeling methods. In: Llad\u00f3s, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12822, pp. 541\u2013553. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86331-9_35"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 894\u2013901 (2019)","DOI":"10.1109\/ICDAR.2019.00148"},{"key":"8_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108565","volume":"126","author":"Z Zhang","year":"2022","unstructured":"Zhang, Z., Zhang, J., Du, J., Wang, F.: Split, embed and merge: an accurate table structure recognizer. Pattern Recognit. 126, 108565 (2022)","journal-title":"Pattern Recognit."},{"key":"8_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110279","volume":"149","author":"Z Zhang","year":"2024","unstructured":"Zhang, Z., et al.: SEMv2: table separation line detection based on instance segmentation. Pattern Recognit. 149, 110279 (2024)","journal-title":"Pattern Recognit."},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Qin, C., Zhang, Z., Hu, P., Liu, C., Ma, J., Du, J.: Semv3: a fast and robust approach to table separation line detection. In: Thirty-Third International Joint Conference on Artificial Intelligence, pp. 1191\u20131199 (2024)","DOI":"10.24963\/ijcai.2024\/132"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Lin, W., et al.: TSRFormer: table structure recognition with transformers. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 6473\u20136482 (2022)","DOI":"10.1145\/3503161.3548038"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Robust Table Structure Recognition with Dynamic Queries Enhanced Detection Transformer. arXiv preprint arXiv:2303.11615 (2023)","DOI":"10.2139\/ssrn.4399248"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Lysak, M., Nassar, A., Livathinos, N., Auer, C., Staar, P.: Optimized table tokenization for table structure recognition. In: 2023 International Conference on Document Analysis and Recognition (ICDAR), pp. 37\u201350 (2023)","DOI":"10.1007\/978-3-031-41679-8_3"},{"key":"8_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109006","volume":"133","author":"C Ma","year":"2023","unstructured":"Ma, C., Lin, W., Sun, L., Huo, Q.: Robust table detection and structure recognition from heterogeneous document images. Pattern Recognit. 133, 109006 (2023)","journal-title":"Pattern Recognit."},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Qasim, S.R., Mahmood, H., Shafait, F.: Rethinking table recognition using graph neural networks. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 142\u2013147 (2019)","DOI":"10.1109\/ICDAR.2019.00031"},{"key":"8_CR23","unstructured":"Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)"},{"key":"8_CR24","doi-asserted-by":"crossref","unstructured":"Long, R., et al.: Parsing table structures in the wild. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 944\u2013952 (2021)","DOI":"10.1109\/ICCV48922.2021.00098"},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Baek, Y., Nam, D., Surh, J., Shin, S., Kim, S.: TRACE: table reconstruction aligned to corner and edges. In: International Conference on Document Analysis and Recognition, pp. 472\u2013489 (2023)","DOI":"10.1007\/978-3-031-41734-4_29"},{"key":"8_CR26","doi-asserted-by":"crossref","unstructured":"Xue, W., Li, Q., Tao, D.: ReS2TIM: reconstruct syntactic structures from table images. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 749\u2013755 (2019)","DOI":"10.1109\/ICDAR.2019.00125"},{"key":"8_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108946","volume":"132","author":"XH Li","year":"2022","unstructured":"Li, X.H., Yin, F., Dai, H.S., Liu, C.L.: Table structure recognition and form parsing by end-to-end object detection and relation parsing. Pattern Recognit. 132, 108946 (2022)","journal-title":"Pattern Recognit."},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.: Show, read and reason: table structure recognition with flexible context aggregator. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 1084\u20131092 (2021)","DOI":"10.1145\/3474085.3481534"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, X., Liu, B., Jiang, D., Liu, Y., Ren, B.: Neural collaborative graph machines for table structure recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4533\u20134542 (2022)","DOI":"10.1109\/CVPR52688.2022.00449"},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.: Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation. arXiv preprint arXiv:2303.09174 (2023)","DOI":"10.1609\/aaai.v38i4.28149"},{"key":"8_CR31","doi-asserted-by":"crossref","unstructured":"Nguyen, N.Q., Pham, X.P., Tran, T.A.: RTSR: a real-time table structure recognition approach. In: ECAI 2024, pp. 681\u2013687 (2024)","DOI":"10.3233\/FAIA240549"},{"key":"8_CR32","doi-asserted-by":"crossref","unstructured":"Smock, B., Pesala, R., Abraham, R.: PubTables-1M: towards comprehensive table extraction from unstructured documents. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4634\u20134642 (2022)","DOI":"10.1109\/CVPR52688.2022.00459"},{"key":"8_CR33","doi-asserted-by":"crossref","unstructured":"Paliwal, S.S., Vishwanath, D., Rahul, R., Sharma, M., Vig, L.: Tablenet: deep learning model for end-to-end table detection and tabular data extraction from scanned document images. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 128\u2013133 (2019)","DOI":"10.1109\/ICDAR.2019.00029"},{"key":"8_CR34","doi-asserted-by":"crossref","unstructured":"Lyu, P., et al.: Gridformer: towards accurate table structure recognition via grid prediction. In Proceedings of the 31st ACM International Conference on Multimedia, pp. 7747\u20137757 (2023)","DOI":"10.1145\/3581783.3611961"},{"key":"8_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1007\/978-3-030-58604-1_5","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Raja","year":"2020","unstructured":"Raja, S., Mondal, A., Jawahar, C.V.: Table structure recognition using top-down and bottom-up cues. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12373, pp. 70\u201386. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58604-1_5"},{"key":"8_CR36","doi-asserted-by":"crossref","unstructured":"Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: Deepdesrt: deep learning for detection and structure recognition of tables in document images. In: 2017 14th IAPR International Conference on Document Analysis and recognition (ICDAR), pp. 1162\u20131167 (2017)","DOI":"10.1109\/ICDAR.2017.192"},{"key":"8_CR37","doi-asserted-by":"crossref","unstructured":"Kawakatsu, T.: Multi-cell decoder and mutual learning for table structure and character recognition. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 389\u2013405 (2024)","DOI":"10.1007\/978-3-031-70533-5_23"},{"key":"8_CR38","doi-asserted-by":"crossref","unstructured":"Nguyen, N.Q., Le, A.D., Lu, A.K., Mai, X.T., Tran, T.A.: Formerge: recover spanning cells in complex table structure using transformer network. In: International Conference on Document Analysis and Recognition, pp. 522\u2013534 (2023)","DOI":"10.1007\/978-3-031-41734-4_32"},{"key":"8_CR39","unstructured":"Guo, Z., et al.: TRUST: An Accurate and End-to-End Table structure Recognizer Using Splitting-based Transformers. arXiv preprint arXiv:2208.14687 (2022)"},{"key":"8_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110816","volume":"157","author":"R Long","year":"2025","unstructured":"Long, R., et al.: LORE++: logical location regression network for table structure recognition with pre-training. Pattern Recognit. 157, 110816 (2025)","journal-title":"Pattern Recognit."}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition \u2013 ICDAR 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04627-7_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T02:07:02Z","timestamp":1757988422000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04627-7_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"ISBN":["9783032046260","9783032046277"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04627-7_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,16]]},"assertion":[{"value":"16 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Document Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdar2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iapr.org\/icdar2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}