{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T08:01:06Z","timestamp":1742976066542,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031416842"},{"type":"electronic","value":"9783031416859"}],"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_9","type":"book-chapter","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T14:04:59Z","timestamp":1692367499000},"page":"131-146","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sampling and\u00a0Ranking for\u00a0Digital Ink Generation on\u00a0a\u00a0Tight Computational Budget"],"prefix":"10.1007","author":[{"given":"Andrei","family":"Afonin","sequence":"first","affiliation":[]},{"given":"Andrii","family":"Maksai","sequence":"additional","affiliation":[]},{"given":"Aleksandr","family":"Timofeev","sequence":"additional","affiliation":[]},{"given":"Claudiu","family":"Musat","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,19]]},"reference":[{"key":"9_CR1","unstructured":"Aksan, E., Deselaers, T., Tagliasacchi, A., Hilliges, O.: Cose: compositional stroke embeddings. arXiv preprint arXiv:2006.09930 (2020)"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Aksan, E., Pece, F., Hilliges, O.: Deepwriting: making digital ink editable via deep generative modeling. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (2018)","DOI":"10.1145\/3173574.3173779"},{"key":"9_CR3","unstructured":"Baevski, A., Auli, M.: Adaptive input representations for neural language modeling. arXiv preprint arXiv:1809.10853 (2018)"},{"key":"9_CR4","doi-asserted-by":"publisher","unstructured":"Basu, S., Ramachandran, G.S., Keskar, N.S., Varshney, L.R.: Mirostat: a neural text decoding algorithm that directly controls perplexity (2020). https:\/\/doi.org\/10.48550\/ARXIV.2007.14966. https:\/\/arxiv.org\/abs\/2007.14966","DOI":"10.48550\/ARXIV.2007.14966"},{"key":"9_CR5","doi-asserted-by":"publisher","unstructured":"Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks (2015). https:\/\/doi.org\/10.48550\/ARXIV.1506.03099.https:\/\/arxiv.org\/abs\/1506.03099","DOI":"10.48550\/ARXIV.1506.03099."},{"key":"9_CR6","unstructured":"Betker, J.: TorToiSe text-to-speech, April 2022. https:\/\/github.com\/neonbjb\/tortoise-tts"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Cao, N., Yan, X., Shi, Y., Chen, C.: AI-sketcher: a deep generative model for producing high-quality sketches. In: Proceedings of the AAAI Conference on Artificial Intelligence (2019)","DOI":"10.1609\/aaai.v33i01.33012564"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Carbune, V., et al.: Fast multi-language LSTM-based online handwriting recognition (2020)","DOI":"10.1007\/s10032-020-00350-4"},{"key":"9_CR9","unstructured":"Chang, J., Shrivastava, A., Koppula, H., Zhang, X., Tuzel, O.: Style equalization: Unsupervised learning of controllable generative sequence models. arXiv preprint arXiv:2110.02891 (2021)"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Chang, J.H.R., et al.: Data incubation-synthesizing missing data for handwriting recognition. In: ICASSP 2022\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4188\u20134192. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9746229"},{"key":"9_CR11","doi-asserted-by":"publisher","unstructured":"Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A., Bengio, Y.: A recurrent latent variable model for sequential data (2015). https:\/\/doi.org\/10.48550\/ARXIV.1506.02216. https:\/\/arxiv.org\/abs\/1506.02216","DOI":"10.48550\/ARXIV.1506.02216"},{"key":"9_CR12","unstructured":"Das, A., Yang, Y., Hospedales, T., Xiang, T., Song, Y.Z.: Sketchode: learning neural sketch representation in continuous time. In: International Conference on Learning Representations (2021)"},{"key":"9_CR13","doi-asserted-by":"publisher","unstructured":"Fan, A., Lewis, M., Dauphin, Y.: Hierarchical neural story generation (2018). https:\/\/doi.org\/10.48550\/ARXIV.1805.04833, https:\/\/arxiv.org\/abs\/1805.04833","DOI":"10.48550\/ARXIV.1805.04833"},{"key":"9_CR14","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315\u2013323. JMLR Workshop and Conference Proceedings (2011)"},{"key":"9_CR15","unstructured":"Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Graves, A., Fern\u00e1ndez, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 369\u2013376 (2006)","DOI":"10.1145\/1143844.1143891"},{"key":"9_CR17","unstructured":"Ha, D., Eck, D.: A neural representation of sketch drawings. arXiv preprint arXiv:1704.03477 (2017)"},{"key":"9_CR18","unstructured":"He, T., Zhang, J., Zhou, Z., Glass, J.R.: Quantifying exposure bias for open-ended language generation (2020)"},{"key":"9_CR19","doi-asserted-by":"publisher","unstructured":"Holtzman, A., Buys, J., Du, L., Forbes, M., Choi, Y.: The curious case of neural text degeneration (2019). https:\/\/doi.org\/10.48550\/ARXIV.1904.09751. https:\/\/arxiv.org\/abs\/1904.09751","DOI":"10.48550\/ARXIV.1904.09751"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Keysers, D., Deselaers, T., Rowley, H., Wang, L., Carbune, V.: Multi-language online handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2017)","DOI":"10.1109\/TPAMI.2016.2572693"},{"key":"9_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1007\/978-3-030-58610-2_45","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Kotani","year":"2020","unstructured":"Kotani, A., Tellex, S., Tompkin, J.: Generating handwriting via decoupled style descriptors. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 764\u2013780. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_45"},{"key":"9_CR22","doi-asserted-by":"publisher","unstructured":"Krishna, K., Chang, Y., Wieting, J., Iyyer, M.: Rankgen: improving text generation with large ranking models (2022). https:\/\/doi.org\/10.48550\/ARXIV.2205.09726. https:\/\/arxiv.org\/abs\/2205.09726","DOI":"10.48550\/ARXIV.2205.09726"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Liu, B., Wei, H., Niu, D., Chen, H., He, Y.: Asking questions the human way: scalable question-answer generation from text corpus. In: Proceedings of the Web Conference 2020, pp. 2032\u20132043 (2020)","DOI":"10.1145\/3366423.3380270"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Liwicki, M., Bunke, H.: IAM-OnDB-an on-line English sentence database acquired from handwritten text on a whiteboard. In: ICDAR 2005. IEEE (2005)","DOI":"10.1109\/ICDAR.2005.132"},{"key":"9_CR25","doi-asserted-by":"publisher","unstructured":"Luhman, T., Luhman, E.: Diffusion models for handwriting generation (2020). https:\/\/doi.org\/10.48550\/ARXIV.2011.06704, https:\/\/arxiv.org\/abs\/2011.06704","DOI":"10.48550\/ARXIV.2011.06704"},{"key":"9_CR26","doi-asserted-by":"publisher","unstructured":"Maksai, A., Rowley, H., Berent, J., Musat, C.: Inkorrect: online handwriting spelling correction (2022). https:\/\/doi.org\/10.48550\/ARXIV.2202.13794. https:\/\/arxiv.org\/abs\/2202.13794","DOI":"10.48550\/ARXIV.2202.13794"},{"key":"9_CR27","doi-asserted-by":"publisher","unstructured":"Meister, C., Pimentel, T., Wiher, G., Cotterell, R.: Typical decoding for natural language generation (2022). https:\/\/doi.org\/10.48550\/ARXIV.2202.00666. https:\/\/arxiv.org\/abs\/2202.00666","DOI":"10.48550\/ARXIV.2202.00666"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Nguyen, H., Nguyen, C., Bao, P., Nakagawa, M.: A database of unconstrained Vietnamese online handwriting and recognition experiments by recurrent neural networks. Pattern Recognition (2018)","DOI":"10.1109\/ICFHR-2018.2018.00082"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Ravaut, M., Joty, S., Chen, N.F.: Summareranker: a multi-task mixture-of-experts re-ranking framework for abstractive summarization. arXiv preprint arXiv:2203.06569 (2022)","DOI":"10.18653\/v1\/2022.acl-long.309"},{"key":"9_CR30","unstructured":"Reddy, R.: Speech understanding systems: A summary of results of the five-year research effort at carnegie mellon university. Tech. rep. (1977)"},{"key":"9_CR31","unstructured":"Ribeiro, L., Bui, T., Collomosse, J., Ponti, M.: Sketchformer: transformer-based representation for sketched structure. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020)"},{"key":"9_CR32","doi-asserted-by":"publisher","unstructured":"See, A., Pappu, A., Saxena, R., Yerukola, A., Manning, C.D.: Do massively pretrained language models make better storytellers? (2019). https:\/\/doi.org\/10.48550\/ARXIV.1909.10705, https:\/\/arxiv.org\/abs\/1909.10705","DOI":"10.48550\/ARXIV.1909.10705"},{"key":"9_CR33","volume-title":"B\u00e9ziersketch: A generative model for scalable vector sketches","author":"Y Song","year":"2020","unstructured":"Song, Y.: B\u00e9ziersketch: A generative model for scalable vector sketches. University of Surrey, Tech. rep. (2020)"},{"key":"9_CR34","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in neural information processing systems (2017)"},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Tacotron: towards end-to-end speech synthesis. arXiv preprint arXiv:1703.10135 (2017)","DOI":"10.21437\/Interspeech.2017-1452"},{"key":"9_CR36","unstructured":"Zhang, T., et al.: Coder reviewer reranking for code generation. arXiv preprint arXiv:2211.16490 (2022)"}],"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_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T14:07:30Z","timestamp":1692367650000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41685-9_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031416842","9783031416859"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41685-9_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}