{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T05:22:32Z","timestamp":1780464152090,"version":"3.54.1"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031023743","type":"print"},{"value":"9783031023750","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-02375-0_2","type":"book-chapter","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T11:03:10Z","timestamp":1652180590000},"page":"17-31","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Offline Handwritten Mathematical Expression Recognition via\u00a0Graph Reasoning Network"],"prefix":"10.1007","author":[{"given":"Jia-Man","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin-Wen","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin-Lin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Alvaro, F., S\u00e1nchez, J.A., Bened\u00ed, J.M.: Offline features for classifying handwritten math symbols with recurrent neural networks. In: 2014 22nd International Conference on Pattern Recognition, pp. 2944\u20132949. IEEE (2014)","DOI":"10.1109\/ICPR.2014.507"},{"key":"2_CR2","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.patrec.2012.09.023","volume":"35","author":"F Alvaro","year":"2014","unstructured":"Alvaro, F., S\u00e1nchez, J.A., Bened\u00ed, J.M.: Recognition of on-line handwritten mathematical expressions using 2D stochastic context-free grammars and hidden Markov models. Pattern Recogn. Lett. 35, 58\u201367 (2014)","journal-title":"Pattern Recogn. Lett."},{"key":"2_CR3","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.patcog.2015.09.013","volume":"51","author":"F \u00c1lvaro","year":"2016","unstructured":"\u00c1lvaro, F., S\u00e1nchez, J.A., Bened\u00ed, J.M.: An integrated grammar-based approach for mathematical expression recognition. Pattern Recogn. 51, 135\u2013147 (2016)","journal-title":"Pattern Recogn."},{"key":"2_CR4","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., Bengio, Y.: End-to-end attention-based large vocabulary speech recognition. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4945\u20134949. IEEE (2016)","DOI":"10.1109\/ICASSP.2016.7472618"},{"key":"2_CR6","doi-asserted-by":"publisher","first-page":"61565","DOI":"10.1109\/ACCESS.2020.2984627","volume":"8","author":"C Chan","year":"2020","unstructured":"Chan, C.: Stroke extraction for offline handwritten mathematical expression recognition. IEEE Access 8, 61565\u201361575 (2020)","journal-title":"IEEE Access"},{"issue":"1","key":"2_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/PL00013549","volume":"3","author":"KF Chan","year":"2000","unstructured":"Chan, K.F., Yeung, D.Y.: Mathematical expression recognition: a survey. Int. J. Doc. Anal. Recogn. 3(1), 3\u201315 (2000). https:\/\/doi.org\/10.1007\/PL00013549","journal-title":"Int. J. Doc. Anal. Recogn."},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Dai, B., Zhang, Y., Lin, D.: Detecting visual relationships with deep relational networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3076\u20133086 (2017)","DOI":"10.1109\/CVPR.2017.352"},{"key":"2_CR9","unstructured":"Deng, Y., Kanervisto, A., Rush, A.M.: What you get is what you see: a visual markup decompiler (2016)"},{"key":"2_CR10","unstructured":"Deng, Y., Kanervisto, A., Ling, J., Rush, A.M.: Image-to-markup generation with coarse-to-fine attention. In: International Conference on Machine Learning, pp. 980\u2013989. PMLR (2017)"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"2_CR12","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":"2_CR13","doi-asserted-by":"crossref","unstructured":"Hu, L., Zanibbi, R.: Line-of-sight stroke graphs and Parzen shape context features for handwritten math formula representation and symbol segmentation. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 180\u2013186. IEEE (2016)","DOI":"10.1109\/ICFHR.2016.0044"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"2_CR15","doi-asserted-by":"publisher","unstructured":"Jocher, G., et al.: ultralytics\/yolov5: v5.0 - YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations, April 2021. https:\/\/doi.org\/10.5281\/zenodo.4679653","DOI":"10.5281\/zenodo.4679653"},{"issue":"2","key":"2_CR16","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s10032-019-00349-6","volume":"23","author":"F Julca-Aguilar","year":"2020","unstructured":"Julca-Aguilar, F., Mouch\u00e8re, H., Viard-Gaudin, C., Hirata, N.S.: A general framework for the recognition of online handwritten graphics. Int. J. Doc. Anal. Recogn. (IJDAR) 23(2), 143\u2013160 (2020)","journal-title":"Int. J. Doc. Anal. Recogn. (IJDAR)"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Le, A.D., Nakagawa, M.: Training an end-to-end system for handwritten mathematical expression recognition by generated patterns. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1056\u20131061. IEEE (2017)","DOI":"10.1109\/ICDAR.2017.175"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Li, L., Tang, S., Deng, L., Zhang, Y., Tian, Q.: Image caption with global-local attention. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.11236"},{"key":"2_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-3-030-57058-3_17","volume-title":"Document Analysis Systems","author":"X-H Li","year":"2020","unstructured":"Li, X.-H., Yin, F., Liu, C.-L.: Page segmentation using convolutional neural network and graphical model. In: Bai, X., Karatzas, D., Lopresti, D. (eds.) DAS 2020. LNCS, vol. 12116, pp. 231\u2013245. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-57058-3_17"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Li, Z., Jin, L., Lai, S., Zhu, Y.: Improving attention-based handwritten mathematical expression recognition with scale augmentation and drop attention. In: 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 175\u2013180. IEEE (2020)","DOI":"10.1109\/ICFHR2020.2020.00041"},{"issue":"2","key":"2_CR21","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s10032-012-0184-x","volume":"16","author":"S MacLean","year":"2013","unstructured":"MacLean, S., Labahn, G.: A new approach for recognizing handwritten mathematics using relational grammars and fuzzy sets. Int. J. Doc. Anal. Recogn. (IJDAR) 16(2), 139\u2013163 (2013). https:\/\/doi.org\/10.1007\/s10032-012-0184-x","journal-title":"Int. J. Doc. Anal. Recogn. (IJDAR)"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Mahdavi, M., Zanibbi, R.: Visual parsing with query-driven global graph attention (QD-GGA): preliminary results for handwritten math formula recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 570\u2013571 (2020)","DOI":"10.1109\/CVPRW50498.2020.00293"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Mouch\u00e8re, H., Viard-Gaudin, C., Zanibbi, R., Garain, U.: ICFHR 2016 CROHME: competition on recognition of online handwritten mathematical expressions. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 607\u2013612. IEEE (2016)","DOI":"10.1109\/ICFHR.2016.0116"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Truong, T.N., Nguyen, C.T., Phan, K.M., Nakagawa, M.: Improvement of end-to-end offline handwritten mathematical expression recognition by weakly supervised learning. In: 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 181\u2013186. IEEE (2020)","DOI":"10.1109\/ICFHR2020.2020.00042"},{"key":"2_CR25","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"2_CR26","doi-asserted-by":"publisher","unstructured":"Wang, D.H., et al.: ICFHR 2020 competition on offline recognition and spotting of handwritten mathematical expressions-OFFRASHME. In: 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 211\u2013215 (2020). https:\/\/doi.org\/10.1109\/ICFHR2020.2020.00047","DOI":"10.1109\/ICFHR2020.2020.00047"},{"key":"2_CR27","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/978-3-030-10925-7_2","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"J-W Wu","year":"2019","unstructured":"Wu, J.-W., Yin, F., Zhang, Y.-M., Zhang, X.-Y., Liu, C.-L.: Image-to-markup generation via paired adversarial learning. In: Berlingerio, M., Bonchi, F., G\u00e4rtner, T., Hurley, N., Ifrim, G. (eds.) ECML PKDD 2018. LNCS (LNAI), vol. 11051, pp. 18\u201334. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-10925-7_2"},{"issue":"10","key":"2_CR28","doi-asserted-by":"publisher","first-page":"2386","DOI":"10.1007\/s11263-020-01291-5","volume":"128","author":"J-W Wu","year":"2020","unstructured":"Wu, J.-W., Yin, F., Zhang, Y.-M., Zhang, X.-Y., Liu, C.-L.: Handwritten mathematical expression recognition via paired adversarial learning. Int. J. Comput. Vis. 128(10), 2386\u20132401 (2020). https:\/\/doi.org\/10.1007\/s11263-020-01291-5","journal-title":"Int. J. Comput. Vis."},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Wu, J.W., Yin, F., Zhang, Y.M., Zhang, X.Y., Liu, C.L.: Graph-to-graph: towards accurate and interpretable online handwritten mathematical expression recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2925\u20132933 (2021)","DOI":"10.1609\/aaai.v35i4.16399"},{"key":"2_CR30","unstructured":"Yamamoto, R., Sako, S., Nishimoto, T., Sagayama, S.: On-line recognition of handwritten mathematical expressions based on stroke-based stochastic context-free grammar. In: Tenth international workshop on frontiers in handwriting recognition. Suvisoft (2006)"},{"key":"2_CR31","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1007\/978-3-030-01246-5_41","volume-title":"Computer Vision \u2013 ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part I","author":"J Yang","year":"2018","unstructured":"Yang, J., Lu, J., Lee, S., Batra, D., Parikh, D.: Graph R-CNN for scene graph\u00a0generation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part I, pp. 690\u2013706. Springer International Publishing, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_41"},{"issue":"4","key":"2_CR32","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s10032-011-0174-4","volume":"15","author":"R Zanibbi","year":"2012","unstructured":"Zanibbi, R., Blostein, D.: Recognition and retrieval of mathematical expressions. Int. J. Doc. Anal. Recogn. (IJDAR) 15(4), 331\u2013357 (2012). https:\/\/doi.org\/10.1007\/s10032-011-0174-4","journal-title":"Int. J. Doc. Anal. Recogn. (IJDAR)"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, J., Du, J., Dai, L.: A GRU-based encoder-decoder approach with attention for online handwritten mathematical expression recognition. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 902\u2013907. IEEE (2017)","DOI":"10.1109\/ICDAR.2017.152"},{"key":"2_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, J., Du, J., Dai, L.: Multi-scale attention with dense encoder for handwritten mathematical expression recognition. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2245\u20132250. IEEE (2018)","DOI":"10.1109\/ICPR.2018.8546031"},{"key":"2_CR35","unstructured":"Zhang, J., Du, J., Yang, Y., Song, Y.Z., Wei, S., Dai, L.: A tree-structured decoder for image-to-markup generation. In: International Conference on Machine Learning, pp. 11076\u201311085. PMLR (2020)"},{"key":"2_CR36","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.patcog.2017.06.017","volume":"71","author":"J Zhang","year":"2017","unstructured":"Zhang, J., et al.: Watch, attend and parse: an end-to-end neural network based approach to handwritten mathematical expression recognition. Pattern Recogn. 71, 196\u2013206 (2017)","journal-title":"Pattern Recogn."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-02375-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,4]],"date-time":"2023-02-04T20:28:29Z","timestamp":1675542509000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-02375-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031023743","9783031023750"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-02375-0_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.acpr2021.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"154","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":"85","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":"55% - 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":"3","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":"4","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)"}}]}}