{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T13:16:59Z","timestamp":1770297419903,"version":"3.49.0"},"publisher-location":"Cham","reference-count":57,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031416750","type":"print"},{"value":"9783031416767","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-41676-7_22","type":"book-chapter","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T07:02:59Z","timestamp":1692342179000},"page":"381-396","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["DTDT: Highly Accurate Dense Text Line Detection in\u00a0Historical Documents via\u00a0Dynamic Transformer"],"prefix":"10.1007","author":[{"given":"Haiyang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chongyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiapeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiying","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lianwen","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,19]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Bi, Y., Hu, Z.: Disentangled contour learning for quadrilateral text detection. In: WACV, pp. 909\u2013918 (2021)","DOI":"10.1109\/WACV48630.2021.00095"},{"issue":"2","key":"22_CR2","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s10032-022-00395-7","volume":"25","author":"M Boillet","year":"2022","unstructured":"Boillet, M., Kermorvant, C., Paquet, T.: Robust text line detection in historical documents: learning and evaluation methods. IJDAR 25(2), 95\u2013114 (2022)","journal-title":"IJDAR"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: CVPR, pp. 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Chen, K., et al.: Hybrid task cascade for instance segmentation. In: CVPR, pp. 4974\u20134983 (2019)","DOI":"10.1109\/CVPR.2019.00511"},{"key":"22_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/978-3-030-58529-7_21","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic ReLU. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12364, pp. 351\u2013367. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58529-7_21"},{"key":"22_CR6","doi-asserted-by":"publisher","unstructured":"Cheng, H., Jian, C., Wu, S., Jin, L.: SCUT-CAB: a new benchmark dataset of ancient Chinese books with complex layouts for document layout analysis. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) ICFHR 2022. LNCS, vol. 13639, pp. 436\u2013451. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-21648-0_30","DOI":"10.1007\/978-3-031-21648-0_30"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Dai, X., Chen, Y., Yang, J., Zhang, P., Yuan, L., Zhang, L.: Dynamic DETR: end-to-end object detection with dynamic attention. In: ICCV, pp. 2988\u20132997 (2021)","DOI":"10.1109\/ICCV48922.2021.00298"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Fang, Y., et al.: Instances as queries. In: ICCV, pp. 6910\u20136919 (2021)","DOI":"10.1109\/ICCV48922.2021.00683"},{"issue":"3","key":"22_CR10","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s10032-019-00332-1","volume":"22","author":"T Gr\u00fcning","year":"2019","unstructured":"Gr\u00fcning, T., Leifert, G., Strau\u00df, T., Michael, J., Labahn, R.: A two-stage method for text line detection in historical documents. Int. J. Doc. Anal. Recogn. (IJDAR) 22(3), 285\u2013302 (2019). https:\/\/doi.org\/10.1007\/s10032-019-00332-1","journal-title":"Int. J. Doc. Anal. Recogn. (IJDAR)"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Haque, M.: A two-dimensional fast cosine transform. IEEE Trans. Acoust., Speech, Signal Process. 33(6), 1532\u20131539 (1985)","DOI":"10.1109\/TASSP.1985.1164737"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: ICCV, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: CVPR, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"22_CR14","doi-asserted-by":"publisher","first-page":"7389","DOI":"10.1109\/TIP.2020.3002345","volume":"29","author":"T Kong","year":"2020","unstructured":"Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: FoveaBox: beyound anchor-based object detection. IEEE Trans. Image Process. 29, 7389\u20137398 (2020)","journal-title":"IEEE Trans. Image Process."},{"issue":"1\u20132","key":"22_CR15","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1002\/nav.3800020109","volume":"2","author":"HW Kuhn","year":"1955","unstructured":"Kuhn, H.W.: The Hungarian method for the assignment problem. Nav. Res. Logist. Q. 2(1\u20132), 83\u201397 (1955)","journal-title":"Nav. Res. Logist. Q."},{"key":"22_CR16","doi-asserted-by":"publisher","unstructured":"Li, X., et al.: Generalized focal loss: learning qualified and distributed bounding boxes for dense object detection. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) NIPS 2020. LNCS, vol. 33, pp. 21002\u201321012. Curran Associates Inc, Red Hook, NY, USA (2020). https:\/\/doi.org\/10.5555\/3495724.3497487","DOI":"10.5555\/3495724.3497487"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Liao, M., Shi, B., Bai, X., Wang, X., Liu, W.: Textboxes: a fast text detector with a single deep neural network. In: AAAI (2017)","DOI":"10.1609\/aaai.v31i1.11196"},{"key":"22_CR18","doi-asserted-by":"publisher","unstructured":"Liao, M., Wan, Z., Yao, C., Chen, K., Bai, X.: Real-time scene text detection with differentiable binarization. In: AAAI, pp. 11474\u201311481 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i07.6812","DOI":"10.1609\/aaai.v34i07.6812"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Liao, M., Zou, Z., Wan, Z., Yao, C., Bai, X.: Real-time scene text detection with differentiable binarization and adaptive scale fusion. TPAMI (2022)","DOI":"10.1109\/TPAMI.2022.3155612"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. In: ICCV, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: CVPR, pp. 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"22_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, S., Jin, L., Xie, L., Wu, Y., Wang, Z.: Omnidirectional scene text detection with sequential-free box discretization. In: IJCAI, pp. 3052\u20133058 (2019)","DOI":"10.24963\/ijcai.2019\/423"},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: ICCV, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"22_CR26","doi-asserted-by":"crossref","unstructured":"Long, S., Ruan, J., Zhang, W., He, X., Wu, W., Yao, C.: TextSnake: a flexible representation for detecting text of arbitrary shapes. In: ECCV, pp. 20\u201336 (2018)","DOI":"10.1007\/978-3-030-01216-8_2"},{"key":"22_CR27","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR (2019)"},{"key":"22_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/978-3-030-01264-9_5","volume-title":"Computer Vision \u2013 ECCV 2018","author":"P Lyu","year":"2018","unstructured":"Lyu, P., Liao, M., Yao, C., Wu, W., Bai, X.: Mask TextSpotter: an end-to-end trainable neural network for spotting text with arbitrary shapes. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 71\u201388. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_5"},{"key":"22_CR29","doi-asserted-by":"crossref","unstructured":"Ma, W., Zhang, H., Jin, L., Wu, S., Wang, J., Wang, Y.: Joint layout analysis, character detection and recognition for historical document digitization. In: ICFHR, pp. 31\u201336 (2020)","DOI":"10.1109\/ICFHR2020.2020.00017"},{"key":"22_CR30","doi-asserted-by":"crossref","unstructured":"Mao, Q., Sun, L., Wu, J., Gao, Y., Wu, X., Qiu, L.: SATMask: spatial attention transform mask for dense instance segmentation. In: DSC, pp. 592\u2013598 (2022)","DOI":"10.1109\/DSC55868.2022.00089"},{"key":"22_CR31","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 3DV, pp. 565\u2013571 (2016)","DOI":"10.1109\/3DV.2016.79"},{"issue":"8","key":"22_CR32","doi-asserted-by":"publisher","first-page":"3676","DOI":"10.1109\/TIP.2018.2825107","volume":"27","author":"BS Minghui Liao","year":"2018","unstructured":"Minghui Liao, B.S., Bai, X.: Textboxes++: a single-shot oriented scene text detector. IEEE Trans. Image Process. 27(8), 3676\u20133690 (2018)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"22_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3573891","volume":"22","author":"SK Mishra","year":"2023","unstructured":"Mishra, S.K., Sinha, S., Saha, S., Bhattacharyya, P.: Dynamic convolution-based-encoder decoder framework for image captioning in Hindi. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 22(4), 1\u201318 (2023)","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inf. Process."},{"key":"22_CR34","doi-asserted-by":"crossref","unstructured":"Raisi, Z., Naiel, M.A., Younes, G., Wardell, S., Zelek, J.S.: Transformer-based text detection in the wild. In: CVPR Workshops, pp. 3162\u20133171 (2021)","DOI":"10.1109\/CVPRW53098.2021.00353"},{"key":"22_CR35","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"22_CR36","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) NIPS 2015. LNCS, vol. 28. Curran Associates, Inc. (2015)"},{"key":"22_CR37","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: CVPR, pp. 658\u2013666 (2019)","DOI":"10.1109\/CVPR.2019.00075"},{"key":"22_CR38","doi-asserted-by":"crossref","unstructured":"Saini, R., Dobson, D., Morrey, J., Liwicki, M., Simistira Liwicki, F.: ICDAR 2019 historical document reading challenge on large structured Chinese family records. In: ICDAR, pp. 1499\u20131504. IEEE (2019)","DOI":"10.1109\/ICDAR.2019.00241"},{"key":"22_CR39","doi-asserted-by":"crossref","unstructured":"Shen, X., et al.: DCT-Mask: discrete cosine transform mask representation for instance segmentation. In: CVPR, pp. 8720\u20138729 (2021)","DOI":"10.1109\/CVPR46437.2021.00861"},{"key":"22_CR40","doi-asserted-by":"crossref","unstructured":"Sun, P., et al.: Sparse R-CNN: end-to-end object detection with learnable proposals. In: CVPR, pp. 14454\u201314463 (2021)","DOI":"10.1109\/CVPR46437.2021.01422"},{"key":"22_CR41","doi-asserted-by":"crossref","unstructured":"Tang, J., et al.: Few could be better than all: feature sampling and grouping for scene text detection. In: CVPR, pp. 4563\u20134572 (2022)","DOI":"10.1109\/CVPR52688.2022.00452"},{"key":"22_CR42","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: ICCV, pp. 9627\u20139636 (2019)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"22_CR43","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shu, M., Lyu, P., Li, R., Zhou, C., Shen, X., Jia, J.: Learning shape-aware embedding for scene text detection. In: CVPR, pp. 4234\u20134243 (2019)","DOI":"10.1109\/CVPR.2019.00436"},{"key":"22_CR44","doi-asserted-by":"publisher","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) NIPS 2017. LNCS, vol. 30, pp. 5998\u20136008. Curran Associates, Inc. (2017). https:\/\/doi.org\/10.5555\/3295222.3295349","DOI":"10.5555\/3295222.3295349"},{"key":"22_CR45","doi-asserted-by":"crossref","unstructured":"Vu, T., Kang, H., Yoo, C.D.: SCNet: training inference sample consistency for instance segmentation. In: AAAI, pp. 2701\u20132709 (2021)","DOI":"10.1609\/aaai.v35i3.16374"},{"key":"22_CR46","doi-asserted-by":"crossref","unstructured":"Wang, F., Chen, Y., Wu, F., Li, X.: TextRay: contour-based geometric modeling for arbitrary-shaped scene text detection. In: ACM MM, pp. 111\u2013119 (2020)","DOI":"10.1145\/3394171.3413819"},{"key":"22_CR47","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Hou, W., Lu, T., Yu, G., Shao, S.: Shape robust text detection with progressive scale expansion network. In: CVPR, pp. 9336\u20139345 (2019)","DOI":"10.1109\/CVPR.2019.00956"},{"key":"22_CR48","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Efficient and accurate arbitrary-shaped text detection with pixel aggregation network. In: ICCV, pp. 8440\u20138449 (2019)","DOI":"10.1109\/ICCV.2019.00853"},{"key":"22_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1007\/978-3-030-58523-5_38","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Wang","year":"2020","unstructured":"Wang, X., Kong, T., Shen, C., Jiang, Y., Li, L.: SOLO: segmenting objects by locations. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12363, pp. 649\u2013665. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58523-5_38"},{"key":"22_CR50","series-title":"LNCS","first-page":"17721","volume-title":"NIPS 2020","author":"X Wang","year":"2020","unstructured":"Wang, X., Zhang, R., Kong, T., Li, L., Shen, C.: SOLOv2: dynamic and fast instance segmentation. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) NIPS 2020. LNCS, vol. 33, pp. 17721\u201317732. Curran Associates Inc, Red Hook, NY, USA (2020)"},{"key":"22_CR51","doi-asserted-by":"crossref","unstructured":"Ye, M., Zhang, J., Zhao, S., Liu, J., Du, B., Tao, D.: DPText-DETR: towards better scene text detection with dynamic points in transformer. In: AAAI (2023)","DOI":"10.1609\/aaai.v37i3.25430"},{"key":"22_CR52","unstructured":"Yuliang, L., Lianwen, J., Shuaitao, Z., Sheng, Z.: Detecting curve text in the wild: new dataset and new solution. arXiv preprint arXiv:1712.02170 (2017)"},{"key":"22_CR53","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/978-3-030-86549-8_8","volume-title":"Document Analysis and Recognition \u2013 ICDAR 2021","author":"P Zhang","year":"2021","unstructured":"Zhang, P., et al.: VSR: a unified framework for document layout analysis combining vision, semantics and relations. In: Llad\u00f3s, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12821, pp. 115\u2013130. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86549-8_8"},{"key":"22_CR54","doi-asserted-by":"crossref","unstructured":"Zhou, X., et al.: East: an efficient and accurate scene text detector. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.283"},{"key":"22_CR55","doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable convnets v2: more deformable, better results. In: CVPR, pp. 9308\u20139316 (2019)","DOI":"10.1109\/CVPR.2019.00953"},{"key":"22_CR56","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. In: ICLR (2021)"},{"key":"22_CR57","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Chen, J., Liang, L., Kuang, Z., Jin, L., Zhang, W.: Fourier contour embedding for arbitrary-shaped text detection. In: CVPR, pp. 3123\u20133131 (2021)","DOI":"10.1109\/CVPR46437.2021.00314"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition - ICDAR 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-41676-7_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T07:07:31Z","timestamp":1692342451000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41676-7_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031416750","9783031416767"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41676-7_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"19 August 2023","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":"San Jos\u00e9, CA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdar2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icdar2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"316","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"154","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"49% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.89","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.50","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Number and type of other papers accepted : IJDAR track papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}