{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T22:01:19Z","timestamp":1787695279406,"version":"build-2784847793"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031416842","type":"print"},{"value":"9783031416859","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\/978-3-031-41685-9_3","type":"book-chapter","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T14:04:59Z","timestamp":1692367499000},"page":"38-53","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Improved Learning for\u00a0Online Handwritten Chinese Text Recognition with\u00a0Convolutional Prototype Network"],"prefix":"10.1007","author":[{"given":"Yi","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng-Lin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,19]]},"reference":[{"issue":"2","key":"3_CR1","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1109\/TPAMI.2004.1262182","volume":"26","author":"C-L Liu","year":"2004","unstructured":"Liu, C.-L., J\u00e4ger, S., Nakagawa, M.: Online recognition of Chinese characters: the state-of-the-art. IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 198\u2013213 (2004)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"3_CR2","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/S0031-3203(00)00018-2","volume":"34","author":"C-L Liu","year":"2001","unstructured":"Liu, C.-L., Nakagawa, M.: Evaluation of prototype learning algorithms for nearest-neighbor classifier in application to handwritten character recognition. Pattern Recognit. 34(3), 601\u2013615 (2001)","journal-title":"Pattern Recognit."},{"issue":"10","key":"3_CR3","doi-asserted-by":"publisher","first-page":"3661","DOI":"10.1016\/j.patcog.2012.04.020","volume":"45","author":"D-H Wang","year":"2012","unstructured":"Wang, D.-H., Liu, C.-L., Zhou, X.-D.: An approach for real-time recognition of online Chinese handwritten sentences. Pattern Recognit. 45(10), 3661\u20133675 (2012)","journal-title":"Pattern Recognit."},{"issue":"10","key":"3_CR4","doi-asserted-by":"publisher","first-page":"2413","DOI":"10.1109\/TPAMI.2013.49","volume":"35","author":"X-D Zhou","year":"2013","unstructured":"Zhou, X.-D., Wang, D.-H., Tian, F., Liu, C.-L., Nakagawa, M.: Handwritten Chinese\/Japanese text recognition using semi-Markov conditional random fields. IEEE Trans. Pattern Anal. Mach. Intell. 35(10), 2413\u20132426 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Wu, Y.-C., Fei, Y., Liu, C.-L.: Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models. Pattern Recognit. 65, 251\u2013264 (2017)","DOI":"10.1016\/j.patcog.2016.12.026"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Wang, Z.-X., Wang, Q.-F., Yin, F., Liu, C.-L.: Weakly supervised learning for over-segmentation based handwritten Chinese text recognition. In: ICFHR 2020, pp. 157\u2013162 (2020)","DOI":"10.1109\/ICFHR2020.2020.00038"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Su, T.-H., Zhang, T., Guan, D.-J., Huang, H.-J.: Off-line recognition of realistic Chinese handwriting using segmentation-free strategy. Pattern Recognit. 42(1), 167\u2013182 (2009)","DOI":"10.1016\/j.patcog.2008.05.012"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Jiang, Z.-W., Ding, X.-Q., Liu, C., Wang, Y.-W.: A novel short merged off-line handwritten Chinese character string segmentation algorithm using hidden Markov model. In: ICDAR 2011, pp. 668\u2013672 (2011)","DOI":"10.1109\/ICDAR.2011.140"},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1016\/j.neucom.2016.07.020","volume":"214","author":"K Jayech","year":"2016","unstructured":"Jayech, K., Mahjoub, M.A., Amara, N.E.B.: Synchronous multi-stream hidden Markov model for offline Arabic handwriting recognition without explicit segmentation. Neurocomputing 214, 958\u2013971 (2016)","journal-title":"Neurocomputing"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Peng, D.-Z., et al.: Recognition of handwritten Chinese text by segmentation: a segment-annotation-free approach. IEEE Trans. Multimed. (2022)","DOI":"10.1109\/TMM.2022.3146771"},{"key":"3_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107102","volume":"100","author":"Z-R Wang","year":"2020","unstructured":"Wang, Z.-R., Jun, D., Wang, J.-M.: Writer-aware CNN for parsimonious HMM-based offline handwritten Chinese text recognition. Pattern Recognit. 100, 107102 (2020)","journal-title":"Pattern Recognit."},{"issue":"1","key":"3_CR12","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.patcog.2012.06.021","volume":"46","author":"C-L Liu","year":"2013","unstructured":"Liu, C.-L., Yin, F., Wang, D.-H., Wang, Q.-F.: Online and offline handwritten Chinese character recognition: benchmarking on new databases. Pattern Recognit. 46(1), 155\u2013162 (2013)","journal-title":"Pattern Recognit."},{"issue":"5","key":"3_CR13","first-page":"2358","volume":"44","author":"H-M Yang","year":"2022","unstructured":"Yang, H.-M., Zhang, X.-Y., Yin, F., Yang, Q., Liu, C.-L.: Convolutional prototype network for open set recognition. IEEE Trans. Pattern Anal. Mach. Intell. 44(5), 2358\u20132370 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"3_CR14","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1109\/TPAMI.2004.104","volume":"26","author":"C-L Liu","year":"2004","unstructured":"Liu, C.-L., Sako, H., Fujisawa, H.: Effects of classifier structures and training regimes on integrated segmentation and recognition of handwritten numeral strings. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1395\u20131407 (2004)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Ciresan, D.C., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: CVPR 2012, pp. 3642\u20133649","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"3_CR16","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.patcog.2016.08.005","volume":"61","author":"X-Y Zhang","year":"2017","unstructured":"Zhang, X.-Y., Bengio, Y., Liu, C.-L.: Online and offline handwritten Chinese character recognition: a comprehensive study and new benchmark. Pattern Recognit. 61, 348\u2013360 (2017)","journal-title":"Pattern Recognit."},{"issue":"8","key":"3_CR17","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.1109\/TPAMI.2007.1090","volume":"29","author":"C-L Liu","year":"2007","unstructured":"Liu, C.-L.: Normalization-cooperated gradient feature extraction for handwritten character recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(8), 1465\u20131469 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Liu, X., Hu, B.-T., Chen, Q.-C., Xiang-Ping, W., You, J.-H.: Stroke sequence-dependent deep convolutional neural network for online handwritten chinese character recognition. IEEE Trans. Neural Networks Learn. Syst. 31(11), 4637\u20134648 (2020)","DOI":"10.1109\/TNNLS.2019.2956965"},{"key":"3_CR19","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.patrec.2019.10.001","volume":"128","author":"H-Q Ren","year":"2019","unstructured":"Ren, H.-Q., Wang, W.-Q., Xi-Wen, Q., Cai, Y.-Q.: A new hybrid-parameter recurrent neural network for online handwritten Chinese character recognition. Pattern Recognit. Lett. 128, 400\u2013406 (2019)","journal-title":"Pattern Recognit. Lett."},{"issue":"8","key":"3_CR20","doi-asserted-by":"publisher","first-page":"1903","DOI":"10.1109\/TPAMI.2017.2732978","volume":"40","author":"Z-C Xie","year":"2018","unstructured":"Xie, Z.-C., Sun, Z.-H., Jin, L.-W., Ni, H., Lyons, T.J.: Learning spatial-semantic context with fully convolutional recurrent network for online handwritten Chinese text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(8), 1903\u20131917 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Chen, K., et al.: A compact CNN-DBLSTM based character model for online handwritten Chinese text recognition. In: ICDAR 2017, pp. 1068\u20131073","DOI":"10.1109\/ICDAR.2017.177"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Qin, X.-H., Zhang, H.-Y., Ke, X., Shen, Z.-H., Qi, S.-M., Liu, K.: Progressive multitask learning network for online Chinese signature segmentation and recognition. In: ICFHR 2022, pp. 153\u2013167","DOI":"10.1007\/978-3-031-21648-0_11"},{"issue":"9","key":"3_CR23","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1016\/0031-3203(94)90010-8","volume":"27","author":"S-W Lee","year":"1994","unstructured":"Lee, S.-W., Song, H.-H.: Optimal design of reference models for large-set handwritten character recognition. Pattern Recognit. 27(9), 1267\u20131274 (1994)","journal-title":"Pattern Recognit."},{"key":"3_CR24","unstructured":"Niu, S.-C., et al.: Towards stable test-time adaptation in dynamic wild world. ICLR (2023)"},{"key":"3_CR25","unstructured":"Liu, C.-L., Kim, I.-J., Kim, J.H.: High accuracy handwritten Chinese character recognition by improved feature matching method. In: ICDAR 1997, pp. 1033\u20131037"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Raghavendra, B.S., Narayanan, C.K., Sita, G., Ramakrishnan, A.G., Sriganesh, M.: Prototype learning methods for online handwriting recognition. In: ICDAR 2005, pp. 287\u2013291","DOI":"10.1109\/ICDAR.2005.193"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Liu, C.-L.: One-vs-all training of prototype classifier for pattern classification and retrieval. In: ICPR 2010, pp. 3328\u20133331","DOI":"10.1109\/ICPR.2010.813"},{"issue":"3","key":"3_CR28","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1016\/j.patcog.2013.04.016","volume":"47","author":"S Impedovo","year":"2014","unstructured":"Impedovo, S., Mangini, F.M., Barbuzzi, D.: A novel prototype generation technique for handwriting digit recognition. Pattern Recognit. 47(3), 1002\u20131010 (2014)","journal-title":"Pattern Recognit."},{"key":"3_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108859","volume":"131","author":"X Ao","year":"2022","unstructured":"Ao, X., Zhang, X.-Y., Liu, C.-L.: Cross-modal prototype learning for zero-shot handwritten character recognition. Pattern Recognit. 131, 108859 (2022)","journal-title":"Pattern Recognit."},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Yang, H.-M., Zhang, X.-Y., Yin, F., Liu, C.-L.: Robust classification with convolutional prototype learning. In: CVPR 2018, pp. 3474\u20133482","DOI":"10.1109\/CVPR.2018.00366"},{"key":"3_CR31","doi-asserted-by":"crossref","unstructured":"Gao, L.-K., Zhang, H., Liu, C-L.: Handwritten text recognition with convolutional prototype network and most aligned frame based CTC training. ICDAR (1), pp. 205\u2013220 (2021)","DOI":"10.1007\/978-3-030-86549-8_14"},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"Yu, M.-M., Zhang, H., Yin, F., Liu, C.-L.: An efficient prototype-based model for handwritten text recognition with multi-loss fusion. In: ICFHR 2022, pp. 404\u2013418","DOI":"10.1007\/978-3-031-21648-0_28"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Heng Zhang, Cheng-Lin Liu: A Lattice-Based Method for Keyword Spotting in Online Chinese Handwriting. ICDAR 2011: 1064\u20131068","DOI":"10.1109\/ICDAR.2011.215"},{"issue":"6","key":"3_CR34","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1109\/34.862202","volume":"22","author":"C-L Liu","year":"2000","unstructured":"Liu, C.-L., Nakagawa, M.: Precise Candidate Selection for Large Character Set Recognition by Confidence Evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 22(6), 636\u2013642 (2000)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR35","doi-asserted-by":"crossref","unstructured":"Jeffrey A. Barnett: Computational Methods for A Mathematical Theory of Evidence. Classic Works of the Dempster-Shafer Theory of Belief Functions 2008: 197\u2013216","DOI":"10.1007\/978-3-540-44792-4_8"},{"key":"3_CR36","doi-asserted-by":"crossref","unstructured":"Liu, C.-L., Yin, F., Wang, D.-H., Wang, Q.-F.: CASIA online and offline Chinese handwriting databases. In: ICDAR 2011, pp. 37\u201341 (2011)","DOI":"10.1109\/ICDAR.2011.17"},{"key":"3_CR37","doi-asserted-by":"crossref","unstructured":"Yin, F., Wang, Q.-F., Zhang, X.-Y., Liu, C.-L.: ICDAR 2013 Chinese handwriting recognition competition. In: ICDAR 2013, pp. 1464\u20131470","DOI":"10.1109\/ICDAR.2013.218"},{"issue":"11","key":"3_CR38","doi-asserted-by":"publisher","first-page":"2298","DOI":"10.1109\/TPAMI.2016.2646371","volume":"39","author":"B-G Shi","year":"2017","unstructured":"Shi, B.-G., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298\u20132304 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"3_CR39","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1016\/j.patcog.2013.12.002","volume":"47","author":"X-D Zhou","year":"2014","unstructured":"Zhou, X.-D., Zhang, Y.-M., Tian, F., Wang, H.-A., Liu, C.-L.: Minimum-risk training for semi-Markov conditional random fields with application to handwritten Chinese\/Japanese text recognition. Pattern Recognit. 47(5), 1904\u20131916 (2014)","journal-title":"Pattern Recognit."},{"key":"3_CR40","doi-asserted-by":"crossref","unstructured":"Peng, D.-Z., Jin, L.-W., Wu, Y.-Q., Wang, Z.-P., Cai, M.-X.: A fast and accurate fully convolutional network for end-to-end handwritten Chinese text segmentation and recognition. In: ICDAR 2019, pp. 25\u201330","DOI":"10.1109\/ICDAR.2019.00014"}],"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\/978-3-031-41685-9_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T14:06:48Z","timestamp":1692367608000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41685-9_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031416842","9783031416859"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41685-9_3","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)"}}]}}