{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:56:03Z","timestamp":1775091363210,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000270","name":"Natural Environment Research Council","doi-asserted-by":"publisher","award":["NE\/S015604\/1"],"award-info":[{"award-number":["NE\/S015604\/1"]}],"id":[{"id":"10.13039\/501100000270","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000270","name":"Natural Environment Research Council","doi-asserted-by":"publisher","award":["NE\/S015604\/1"],"award-info":[{"award-number":["NE\/S015604\/1"]}],"id":[{"id":"10.13039\/501100000270","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100031278","name":"Department for Science, Innovation and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100031278","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["IJDAR"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Digitizing historical tabular records is essential for preserving and analyzing valuable data across various fields, but it presents challenges due to complex layouts, mixed text types, and degraded document quality. This paper introduces a comprehensive framework to address these issues through three key contributions. First, it presents , a novel dataset of 1,113 historical logbooks with over 594,000 annotated text cells, designed to handle the complexities of handwritten entries, aging artifacts, and intricate layouts. Second, it proposes a novel context-aware text extraction approach () to reduce cascading errors during table digitization. Third, it proposes an enhanced end-to-end OCR pipeline that integrates  with ByT5, combining OCR and post-OCR correction in a unified training framework. This framework enables the system to produce both the raw OCR output and a corrected version in a single pass, improving recognition accuracy, particularly for multilingual and degraded text, within complex table digitization tasks. The model achieves superior performance with a 0.049 word error rate and a 0.035 character error rate, outperforming existing methods by up to 41% in OCR tasks and 10.74% in table reconstruction tasks. This framework offers a robust solution for large-scale digitization of tabular documents, extending its applications beyond climate records to other domains requiring structured document preservation. The dataset and implementation are available as open-source resources.<\/jats:p>","DOI":"10.1007\/s10032-025-00543-9","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T04:36:07Z","timestamp":1751344567000},"page":"357-376","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Tabular context-aware optical character recognition and tabular data reconstruction for historical records"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7594-6146","authenticated-orcid":false,"given":"Loitongbam Gyanendro","family":"Singh","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8305-8176","authenticated-orcid":false,"given":"Stuart E.","family":"Middleton","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"543_CR1","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012)"},{"key":"543_CR2","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"543_CR3","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neunet.2021.07.003","volume":"143","author":"A Jalali","year":"2021","unstructured":"Jalali, A., Kavuri, S., Lee, M.: Low-shot transfer with attention for highly imbalanced cursive character recognition. Neural Netw. 143, 489\u2013499 (2021)","journal-title":"Neural Netw."},{"key":"543_CR4","doi-asserted-by":"crossref","unstructured":"KO, M.A., Poruran, S.: Ocr-nets: variants of pre-trained cnn for urdu handwritten character recognition via transfer learning. Procedia computer science 171, 2294\u20132301 (2020)","DOI":"10.1016\/j.procs.2020.04.248"},{"key":"543_CR5","doi-asserted-by":"crossref","unstructured":"Li, M., Lv, T., Chen, J., Cui, L., Lu, Y., Florencio, D., Zhang, C., Li, Z., Wei, F.: Trocr: Transformer-based optical character recognition with pre-trained models. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 13094\u201313102 (2023)","DOI":"10.1609\/aaai.v37i11.26538"},{"key":"543_CR6","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). Springer","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"543_CR7","doi-asserted-by":"crossref","unstructured":"Prasad, D., Gadpal, A., Kapadni, K., Visave, M., Sultanpure, K.: Cascadetabnet: 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, pp. 572\u2013573 (2020)","DOI":"10.1109\/CVPRW50498.2020.00294"},{"key":"543_CR8","doi-asserted-by":"publisher","unstructured":"Singh, L.G., Middleton, S.E.: Data rescue for historical document tables using semi-supervised learning (2024). https:\/\/doi.org\/10.21203\/rs.3.rs-4391424\/v1","DOI":"10.21203\/rs.3.rs-4391424\/v1"},{"key":"543_CR9","doi-asserted-by":"crossref","unstructured":"Anand, A., Jaiswal, R., Bhuyan, P., Gupta, M., Bangar, S., Imam, M.M., Shah, R.R., Satoh, S.: Tc-ocr: Tablecraft ocr for efficient detection & recognition of table structure & content. In: Proceedings of the 1st International Workshop on Deep Multimodal Learning for Information Retrieval, pp. 11\u201318 (2023)","DOI":"10.1145\/3606040.3617444"},{"key":"543_CR10","doi-asserted-by":"crossref","unstructured":"Lehenmeier, C., Burghardt, M., Mischka, B.: Layout detection and table recognition\u2013recent challenges in digitizing historical documents and handwritten tabular data. In: Digital Libraries for Open Knowledge: 24th International Conference on Theory and Practice of Digital Libraries, TPDL 2020, Lyon, France, August 25\u201327, 2020, Proceedings 24, pp. 229\u2013242 (2020). Springer","DOI":"10.1007\/978-3-030-54956-5_17"},{"key":"543_CR11","doi-asserted-by":"crossref","unstructured":"Kim, G., Yokoo, S., Seo, S., Osanai, A., Okamoto, Y., Baek, Y.: On text localization in end-to-end ocr-free document understanding transformer without text localization supervision. In: International Conference on Document Analysis and Recognition, pp. 215\u2013232 (2023). Springer","DOI":"10.1007\/978-3-031-41498-5_16"},{"key":"543_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, H., Whittaker, E., Kitagishi, I.: Extending trocr for text localization-free ocr of full-page scanned receipt images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1479\u20131485 (2023)","DOI":"10.1109\/ICCVW60793.2023.00160"},{"key":"543_CR13","doi-asserted-by":"crossref","unstructured":"Chen, Y.-H., Str\u00f6bel, P.B.: Trocr meets language models: An end-to-end post-correction approach. In: International Conference on Document Analysis and Recognition, pp. 12\u201326 (2024). Springer","DOI":"10.1007\/978-3-031-70645-5_2"},{"key":"543_CR14","doi-asserted-by":"crossref","unstructured":"Seth, D., Stureborg, R., Pruthi, D., Dhingra, B.: Learning the legibility of visual text perturbations. In: Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pp. 3260\u20133273 (2023)","DOI":"10.18653\/v1\/2023.eacl-main.238"},{"key":"543_CR15","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1162\/tacl_a_00461","volume":"10","author":"L Xue","year":"2022","unstructured":"Xue, L., Barua, A., Constant, N., Al-Rfou, R., Narang, S., Kale, M., Roberts, A., Raffel, C.: Byt5: Towards a token-free future with pre-trained byte-to-byte models. Transactions of the Association for Computational Linguistics 10, 291\u2013306 (2022)","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"543_CR16","unstructured":"Klink, S., Dengel, A., Kieninger, T.: Document structure analysis based on layout and textual features. In: Proc. of International Workshop on Document Analysis Systems, DAS2000, pp. 99\u2013111 (2000)"},{"key":"543_CR17","doi-asserted-by":"crossref","unstructured":"Oro, E., Ruffolo, M.: Trex: An approach for recognizing and extracting tables from pdf documents. In: 2009 10th International Conference on Document Analysis and Recognition, pp. 906\u2013910 (2009). IEEE","DOI":"10.1109\/ICDAR.2009.12"},{"key":"543_CR18","first-page":"1","volume":"7","author":"R Zanibbi","year":"2004","unstructured":"Zanibbi, R., Blostein, D., Cordy, J.R.: A survey of table recognition: Models, observations, transformations, and inferences. Document Analysis and Recognition 7, 1\u201316 (2004)","journal-title":"Document Analysis and Recognition"},{"key":"543_CR19","doi-asserted-by":"crossref","unstructured":"Huang, Y., Lv, T., Cui, L., Lu, Y., Wei, F.: Layoutlmv3: Pre-training for document ai with unified text and image masking. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 4083\u20134091 (2022)","DOI":"10.1145\/3503161.3548112"},{"key":"543_CR20","doi-asserted-by":"crossref","unstructured":"Xu, Y., Li, M., Cui, L., Huang, S., Wei, F., Zhou, M.: Layoutlm: Pre-training of text and layout for document image understanding. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1192\u20131200 (2020)","DOI":"10.1145\/3394486.3403172"},{"key":"543_CR21","doi-asserted-by":"crossref","unstructured":"Oliveira, D.A.B., Viana, M.P.: Fast cnn-based document layout analysis. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 1173\u20131180 (2017). IEEE","DOI":"10.1109\/ICCVW.2017.142"},{"key":"543_CR22","doi-asserted-by":"crossref","unstructured":"Fang, J., Mitra, P., Tang, Z., Giles, C.L.: Table header detection and classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 26, pp. 599\u2013605 (2012)","DOI":"10.1609\/aaai.v26i1.8206"},{"key":"543_CR23","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). IEEE","DOI":"10.1109\/ICDAR.2019.00029"},{"key":"543_CR24","doi-asserted-by":"crossref","unstructured":"Ziomek, J., Middleton, S.E.: Glosat historical measurement table dataset: enhanced table structure recognition annotation for downstream historical data rescue. In: Proceedings of the 6th International Workshop on Historical Document Imaging and Processing, pp. 49\u201354 (2021)","DOI":"10.1145\/3476887.3476890"},{"key":"543_CR25","unstructured":"Du, Y., Li, C., Guo, R., Cui, C., Liu, W., Zhou, J., Lu, B., Yang, Y., Liu, Q., Hu, X., et al.: Pp-ocrv2: Bag of tricks for ultra lightweight ocr system. arXiv preprint arXiv:2109.03144 (2021)"},{"key":"543_CR26","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), vol. 1, pp. 1162\u20131167 (2017). IEEE","DOI":"10.1109\/ICDAR.2017.192"},{"key":"543_CR27","doi-asserted-by":"crossref","unstructured":"Nassar, A., Livathinos, N., Lysak, M., Staar, P.: Tableformer: Table structure understanding with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4614\u20134623 (2022)","DOI":"10.1109\/CVPR52688.2022.00457"},{"key":"543_CR28","unstructured":"Greif, G., Griesshaber, N., Greif, R.: Multimodal llms for ocr, ocr post-correction, and named entity recognition in historical documents. arXiv preprint arXiv:2504.00414 (2025)"},{"key":"543_CR29","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":"543_CR30","doi-asserted-by":"crossref","unstructured":"Smock, B., Pesala, R., Abraham, R.: Aligning benchmark datasets for table structure recognition. In: International Conference on Document Analysis and Recognition, pp. 371\u2013386 (2023). Springer","DOI":"10.1007\/978-3-031-41734-4_23"},{"key":"543_CR31","unstructured":"Peng, S., Chakravarthy, A., Lee, S., Wang, X., Balasubramaniyan, R., Chau, D.H.: Unitable: Towards a unified framework for table recognition via self-supervised pretraining. In: NeurIPS 2024 Third Table Representation Learning Workshop (2024)"},{"key":"543_CR32","doi-asserted-by":"publisher","unstructured":"Ma, W., Cui, Y., Si, C., Liu, T., Wang, S., Hu, G.: CharBERT: Character-aware pre-trained language model. In: Scott, D., Bel, N., Zong, C. (eds.) Proceedings of the 28th International Conference on Computational Linguistics, pp. 39\u201350 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.4","DOI":"10.18653\/v1\/2020.coling-main.4"},{"key":"543_CR33","doi-asserted-by":"crossref","unstructured":"Rakshit, A., Mehta, S., Dasgupta, A.: A novel pipeline for improving optical character recognition through post-processing using natural language processing. In: 2023 IEEE Guwahati Subsection Conference (GCON), pp. 01\u201306 (2023). IEEE","DOI":"10.1109\/GCON58516.2023.10183509"},{"key":"543_CR34","doi-asserted-by":"publisher","unstructured":"Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., Zettlemoyer, L.: BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871\u20137880 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.703","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"543_CR35","doi-asserted-by":"crossref","unstructured":"G\u00f6bel, M., Hassan, T., Oro, E., Orsi, G.: Icdar 2013 table competition. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1449\u20131453 (2013). IEEE","DOI":"10.1109\/ICDAR.2013.292"},{"key":"543_CR36","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":"543_CR37","doi-asserted-by":"crossref","unstructured":"Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 697\u2013706 (2021)","DOI":"10.1109\/WACV48630.2021.00074"},{"key":"543_CR38","doi-asserted-by":"crossref","unstructured":"Zhong, X., ShafieiBavani, E., Jimeno\u00a0Yepes, A.: Image-based table recognition: data, model, and evaluation. In: European Conference on Computer Vision, pp. 564\u2013580 (2020). Springer","DOI":"10.1007\/978-3-030-58589-1_34"},{"key":"543_CR39","unstructured":"Park, S., Shin, S., Lee, B., Lee, J., Surh, J., Seo, M., Lee, H.: Cord: a consolidated receipt dataset for post-ocr parsing. In: Workshop on Document Intelligence at NeurIPS 2019 (2019)"},{"key":"543_CR40","doi-asserted-by":"crossref","unstructured":"Huang, Z., Chen, K., He, J., Bai, X., Karatzas, D., Lu, S., Jawahar, C.: Icdar 2019 robust reading challenge on scanned receipts ocr and information extraction. In: International Conference on Document Analysis Recognition (2019)","DOI":"10.1109\/ICDAR.2019.00244"},{"key":"543_CR41","doi-asserted-by":"crossref","unstructured":"Fang, S., Xie, H., Wang, Y., Mao, Z., Zhang, Y.: Read like humans: Autonomous, bidirectional and iterative language modeling for scene text recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7098\u20137107 (2021)","DOI":"10.1109\/CVPR46437.2021.00702"},{"key":"543_CR42","doi-asserted-by":"crossref","unstructured":"Karatzas, D., Gomez-Bigorda, L., Nicolaou, A., Ghosh, S., Bagdanov, A., Iwamura, M., Matas, J., Neumann, L., Chandrasekhar, V.R., Lu, S., et al: Icdar 2015 competition on robust reading. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1156\u20131160 (2015). IEEE","DOI":"10.1109\/ICDAR.2015.7333942"},{"key":"543_CR43","doi-asserted-by":"crossref","unstructured":"Kim, G., Hong, T., Yim, M., Nam, J., Park, J., Yim, J., Hwang, W., Yun, S., Han, D., Park, S.: Ocr-free document understanding transformer. In: European Conference on Computer Vision, pp. 498\u2013517 (2022). Springer","DOI":"10.1007\/978-3-031-19815-1_29"},{"key":"543_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108946","volume":"132","author":"X-H 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 Recogn. 132, 108946 (2022)","journal-title":"Pattern Recogn."},{"key":"543_CR45","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"issue":"10","key":"543_CR46","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2020","unstructured":"Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3349\u20133364 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"543_CR47","doi-asserted-by":"crossref","unstructured":"Gao, L., Huang, Y., D\u00e9jean, H., Meunier, J.-L., Yan, Q., Fang, Y., Kleber, F., Lang, E.: Icdar 2019 competition on table detection and recognition (ctdar). In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1510\u20131515 (2019). IEEE","DOI":"10.1109\/ICDAR.2019.00243"},{"key":"543_CR48","unstructured":"Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74\u201381 (2004)"},{"key":"543_CR49","doi-asserted-by":"crossref","unstructured":"Carrasco, R.C.: An open-source ocr evaluation tool. In: Proceedings of the First International Conference on Digital Access to Textual Cultural Heritage, pp. 179\u2013184 (2014)","DOI":"10.1145\/2595188.2595221"},{"key":"543_CR50","doi-asserted-by":"crossref","unstructured":"Rajpurkar, P.: Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)","DOI":"10.18653\/v1\/D16-1264"}],"container-title":["International Journal on Document Analysis and Recognition (IJDAR)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10032-025-00543-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10032-025-00543-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10032-025-00543-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T08:38:56Z","timestamp":1758357536000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10032-025-00543-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":50,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["543"],"URL":"https:\/\/doi.org\/10.1007\/s10032-025-00543-9","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5462018\/v1","asserted-by":"object"}]},"ISSN":["1433-2833","1433-2825"],"issn-type":[{"value":"1433-2833","type":"print"},{"value":"1433-2825","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,1]]},"assertion":[{"value":"15 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}