{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T06:35:17Z","timestamp":1775975717839,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,8,20]],"date-time":"2022-08-20T00:00:00Z","timestamp":1660953600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,8,20]],"date-time":"2022-08-20T00:00:00Z","timestamp":1660953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61936003"],"award-info":[{"award-number":["61936003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2017A030312006"],"award-info":[{"award-number":["2017A030312006"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s11263-022-01654-0","type":"journal-article","created":{"date-parts":[[2022,8,20]],"date-time":"2022-08-20T20:05:33Z","timestamp":1661025933000},"page":"2623-2645","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition"],"prefix":"10.1007","volume":"130","author":[{"given":"Dezhi","family":"Peng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5456-0957","authenticated-orcid":false,"given":"Lianwen","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Yuliang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Canjie","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Songxuan","family":"Lai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,20]]},"reference":[{"key":"1654_CR1","doi-asserted-by":"crossref","unstructured":"Baek, Y., Lee, B., Han, D., Yun, S., Lee, H. (2019). Character region awareness for text detection. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 9365\u20139374.","DOI":"10.1109\/CVPR.2019.00959"},{"key":"1654_CR2","unstructured":"Bluche, T. (2016). Joint line segmentation and transcription for end-to-end handwritten paragraph recognition. In: Proceedings of advances in neural information processing systems, pp. 838\u2013846."},{"key":"1654_CR3","first-page":"1050","volume":"01","author":"T Bluche","year":"2017","unstructured":"Bluche, T., Louradour, J., & Messina, R. (2017). Scan, attend and read: End-to-end handwritten paragraph recognition with MDLSTM attention. Proceedings of International Conference on Document Analysis and Recognition, 01, 1050\u20131055.","journal-title":"Proceedings of International Conference on Document Analysis and Recognition"},{"key":"1654_CR4","first-page":"29","volume":"5","author":"M Carbonell","year":"2019","unstructured":"Carbonell, M., Mas, J., Villegas, M., Forn\u00e9s, A., & Llad\u00f3s, J. (2019). End-to-end handwritten text detection and transcription in full pages. Proceedings of International Conference on Document Analysis and Recognition Workshops, 5, 29\u201334.","journal-title":"Proceedings of International Conference on Document Analysis and Recognition Workshops"},{"key":"1654_CR5","first-page":"35","volume":"5","author":"J Chung","year":"2019","unstructured":"Chung, J., & Delteil, T. (2019). A computationally efficient pipeline approach to full page offline handwritten text recognition. Proceedings of International Conference on Document Analysis and Recognition Workshops, 5, 35\u201340.","journal-title":"Proceedings of International Conference on Document Analysis and Recognition Workshops"},{"key":"1654_CR6","doi-asserted-by":"crossref","unstructured":"Du, J., Zi-Rui, Wang, Zhai, J., Hu, J. (2016). Deep neural network based hidden Markov model for offline handwritten Chinese text recognition. In: Proceedings of IEEE international conference on pattern recognition, pp. 3428\u20133433.","DOI":"10.1109\/ICPR.2016.7900164"},{"issue":"3","key":"1654_CR7","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1007\/s11263-020-01388-x","volume":"129","author":"W Feng","year":"2021","unstructured":"Feng, W., Yin, F., Zhang, X. Y., He, W., & Liu, C. L. (2021). Residual dual scale scene text spotting by fusing bottom-up and top-down processing. International Journal of Computer Vision, 129(3), 619\u2013637.","journal-title":"International Journal of Computer Vision"},{"key":"1654_CR8","doi-asserted-by":"crossref","unstructured":"Graves, A., Fern\u00e1ndez, S., Gomez, F., Schmidhuber, J. (2006). Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of international conference on machine learning, pp. 369\u2013376.","DOI":"10.1145\/1143844.1143891"},{"key":"1654_CR9","unstructured":"Graves, A., Jaitly, N. (2014). Towards end-to-end speech recognition with recurrent neural networks. In: Proceedings of international conference on machine learning, pp. 1764\u20131772."},{"issue":"5","key":"1654_CR10","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1109\/TPAMI.2008.137","volume":"31","author":"A Graves","year":"2009","unstructured":"Graves, A., Liwicki, M., Fern\u00e1ndez, S., Bertolami, R., Bunke, H., & Schmidhuber, J. (2009). A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5), 855\u2013868.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1654_CR11","doi-asserted-by":"crossref","unstructured":"Gupta, A., Vedaldi, A., Zisserman, A. (2016). Synthetic data for text localisation in natural images. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 2315\u20132324.","DOI":"10.1109\/CVPR.2016.254"},{"key":"1654_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., Girshick, R. (2017). Mask R-CNN. In: Proceedings of IEEE international conference on computer vision, pp. 2961\u20132969.","DOI":"10.1109\/ICCV.2017.322"},{"key":"1654_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1654_CR14","doi-asserted-by":"crossref","unstructured":"Huang, Y., Xie, Z., Jin, L., Zhu, Y., Zhang, S. (2019). Adversarial feature enhancing network for end-to-end handwritten paragraph recognition. In: Proceedings of international conference on document analysis and recognition, pp 413\u2013419.","DOI":"10.1109\/ICDAR.2019.00073"},{"key":"1654_CR15","unstructured":"Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A. (2014). Synthetic data and artificial neural networks for natural scene text recognition. In: Proceedings of advances in neural information processing systems deep learn. Workshop."},{"issue":"1","key":"1654_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-015-0823-z","volume":"116","author":"M Jaderberg","year":"2016","unstructured":"Jaderberg, M., Simonyan, K., Vedaldi, A., & Zisserman, A. (2016). Reading text in the wild with convolutional neural networks. International Journal of Computer Vision, 116(1), 1\u201320.","journal-title":"International Journal of Computer Vision"},{"issue":"6","key":"1654_CR17","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1109\/TPAMI.2016.2572693","volume":"39","author":"D Keysers","year":"2017","unstructured":"Keysers, D., Deselaers, T., Rowley, H. A., Wang, L., & Carbune, V. (2017). Multi-language online handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1180\u20131194.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"2","key":"1654_CR18","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TPAMI.2019.2937086","volume":"43","author":"M Liao","year":"2021","unstructured":"Liao, M., Lyu, P., He, M., Yao, C., Wu, W., & Bai, X. (2021). Mask TextSpotter: An end-to-end trainable neural network for spotting text with arbitrary shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(2), 532\u2013548.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1654_CR19","doi-asserted-by":"crossref","unstructured":"Liu, X., Liang, D., Yan, S., Chen, D., Qiao, Y., Yan, J. (2018). FOTS: Fast oriented text spotting with a unified network. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 5676\u20135685.","DOI":"10.1109\/CVPR.2018.00595"},{"key":"1654_CR20","doi-asserted-by":"crossref","unstructured":"Liu, C., Yin, F., Wang, D., Wang, Q. (2011). CASIA online and offline Chinese handwriting databases. In: Proceedings of international conference on document analysis and recognition, pp. 37\u201341.","DOI":"10.1109\/ICDAR.2011.17"},{"issue":"6","key":"1654_CR21","doi-asserted-by":"publisher","first-page":"1972","DOI":"10.1007\/s11263-021-01459-7","volume":"129","author":"Y Liu","year":"2021","unstructured":"Liu, Y., He, T., Chen, H., Wang, X., Luo, C., Zhang, S., et al. (2021). Exploring the capacity of an orderless box discretization network for multi-orientation scene text detection. International Journal of Computer Vision, 129(6), 1972\u20131992.","journal-title":"International Journal of Computer Vision"},{"key":"1654_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-020-01298-y","volume":"128","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Lin, G., & Goh, W. L. (2020). Bottom-up scene text detection with Markov clustering networks. International Journal of Computer Vision, 128, 1\u201324.","journal-title":"International Journal of Computer Vision"},{"issue":"4","key":"1654_CR23","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1007\/s11263-020-01411-1","volume":"129","author":"C Luo","year":"2021","unstructured":"Luo, C., Lin, Q., Liu, Y., Jin, L., & Shen, C. (2021). Separating content from style using adversarial learning for recognizing text in the wild. International Journal of Computer Vision, 129(4), 960\u2013976.","journal-title":"International Journal of Computer Vision"},{"key":"1654_CR24","doi-asserted-by":"crossref","unstructured":"Lyu, P., Liao, M., Yao, C., Wu, W., Bai, X. (2018). Mask TextSpotter: An end-to-end trainable neural network for spotting text with arbitrary shapes. In: proceedings of european conference on computer vision, pp. 67\u201383.","DOI":"10.1007\/978-3-030-01264-9_5"},{"key":"1654_CR25","doi-asserted-by":"crossref","unstructured":"Ma, W., Zhang, H., Jin, L., Wu, S., Wang, J., Wang, Y. (2020). Joint layout analysis, character detection and recognition for historical document digitization. In: Proceedings of international conference on frontiers in handwriting recognition, pp. 31\u201336.","DOI":"10.1109\/ICFHR2020.2020.00017"},{"key":"1654_CR26","doi-asserted-by":"crossref","unstructured":"Messina, R., Louradour, J. (2015). Segmentation-free handwritten Chinese text recognition with LSTM-RNN. In: Proceedings of international conference on document analysis and recognition, pp. 171\u2013175.","DOI":"10.1109\/ICDAR.2015.7333746"},{"key":"1654_CR27","first-page":"871","volume":"01","author":"B Moysset","year":"2017","unstructured":"Moysset, B., Kermorvant, C., & Wolf, C. (2017). Full-page text recognition: Learning where to start and when to stop. Proceedings of International Conference on Document Analysis and Recognition, 01, 871\u2013876.","journal-title":"Proceedings of International Conference on Document Analysis and Recognition"},{"key":"1654_CR28","doi-asserted-by":"crossref","unstructured":"Neubeck, A., Van\u00a0Gool, L. (2006). Efficient non-maximum suppression. In: Proceedings of IEEE international conference on pattern recognition, pp. 850\u2013855.","DOI":"10.1109\/ICPR.2006.479"},{"key":"1654_CR29","doi-asserted-by":"crossref","unstructured":"Peng, D., Jin, L., Wu, Y., Wang, Z., Cai, M. (2019). A fast and accurate fully convolutional network for end-to-end handwritten Chinese text segmentation and recognition. In: Proceedings of international conference on document analysis and recognition, pp. 25\u201330.","DOI":"10.1109\/ICDAR.2019.00014"},{"key":"1654_CR30","unstructured":"Redmon, J., Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767"},{"issue":"6","key":"1654_CR31","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137\u20131149.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"3","key":"1654_CR32","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s11263-014-0793-6","volume":"113","author":"JA Rodriguez-Serrano","year":"2015","unstructured":"Rodriguez-Serrano, J. A., Gordo, A., & Perronnin, F. (2015). Label embedding: A frugal baseline for text recognition. International Journal of Computer Vision, 113(3), 193\u2013207.","journal-title":"International Journal of Computer Vision"},{"issue":"11","key":"1654_CR33","doi-asserted-by":"publisher","first-page":"2298","DOI":"10.1109\/TPAMI.2016.2646371","volume":"39","author":"B Shi","year":"2017","unstructured":"Shi, B., Bai, X., & Yao, C. (2017). An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(11), 2298\u20132304.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"1654_CR34","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.patcog.2008.05.012","volume":"42","author":"TH Su","year":"2009","unstructured":"Su, T. H., Zhang, T. W., Guan, D. J., & Huang, H. J. (2009). Off-line recognition of realistic Chinese handwriting using segmentation-free strategy. Pattern Recognition, 42(1), 167\u2013182.","journal-title":"Pattern Recognition"},{"key":"1654_CR35","doi-asserted-by":"crossref","unstructured":"Tensmeyer, C., Wigington, C. (2019). Training full-page handwritten text recognition models without annotated line breaks. In: Proceedings of international conference on document analysis and recognition, pp. 1\u20138.","DOI":"10.1109\/ICDAR.2019.00011"},{"key":"1654_CR36","doi-asserted-by":"crossref","unstructured":"Wang, S., Chen, L., Xu, L., Fan, W., Sun, J., Naoi, S. (2016). Deep knowledge training and heterogeneous CNN for handwritten Chinese text recognition. In: Proceedings of international conference on frontiers in handwriting recognition, pp. 84\u201389.","DOI":"10.1109\/ICFHR.2016.0028"},{"key":"1654_CR37","doi-asserted-by":"crossref","unstructured":"Wang, Z.X., Wang, Q.F., Yin, F., Liu, C.L. (2020b). Weakly supervised learning for over-segmentation based handwritten Chinese text recognition. In: Proceedings of international conference on frontiers in handwriting recognition, pp. 157\u2013162.","DOI":"10.1109\/ICFHR2020.2020.00038"},{"key":"1654_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107102","volume":"100","author":"ZR Wang","year":"2020","unstructured":"Wang, Z. R., Du, J., & Wang, J. M. (2020). Writer-aware CNN for parsimonious HMM-based offline handwritten Chinese text recognition. Pattern Recognition, 100, 107102.","journal-title":"Pattern Recognition"},{"issue":"4","key":"1654_CR39","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s10032-018-0307-0","volume":"21","author":"ZR Wang","year":"2018","unstructured":"Wang, Z. R., Du, J., Wang, W. C., Zhai, J. F., & Hu, J. S. (2018). A comprehensive study of hybrid neural network hidden Markov model for offline handwritten Chinese text recognition. International Journal on Document Analysis and Recognition, 21(4), 241\u2013251.","journal-title":"International Journal on Document Analysis and Recognition"},{"issue":"8","key":"1654_CR40","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1109\/TPAMI.2011.264","volume":"34","author":"Q Wang","year":"2012","unstructured":"Wang, Q., Yin, F., & Liu, C. (2012). Handwritten Chinese text recognition by integrating multiple contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(8), 1469\u20131481.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1654_CR41","doi-asserted-by":"crossref","unstructured":"Wigington, C., Tensmeyer, C., Davis, B., Barrett, W., Price, B., Cohen, S. (2018). Start, Follow, Read: End-to-end full-page handwriting recognition. In: Proceedings of European conference on computer vision, pp. 367\u2013383.","DOI":"10.1007\/978-3-030-01231-1_23"},{"key":"1654_CR42","first-page":"79","volume":"01","author":"Y Wu","year":"2017","unstructured":"Wu, Y., Yin, F., Chen, Z., & Liu, C. (2017). Handwritten Chinese text recognition using separable multi-dimensional recurrent neural network. Proceedings of International Conference on Document Analysis and Recognition, 01, 79\u201384.","journal-title":"Proceedings of International Conference on Document Analysis and Recognition"},{"key":"1654_CR43","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.patcog.2016.12.026","volume":"65","author":"YC Wu","year":"2017","unstructured":"Wu, Y. C., Yin, F., & Liu, C. L. (2017). Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models. Pattern Recognition, 65, 251\u2013264.","journal-title":"Pattern Recognition"},{"key":"1654_CR44","doi-asserted-by":"crossref","unstructured":"Xie, Z., Huang, Y., Zhu, Y., Jin, L., Liu, Y., Xie, L. (2019b). Aggregation cross-entropy for sequence recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 6538\u20136547.","DOI":"10.1109\/CVPR.2019.00670"},{"key":"1654_CR45","doi-asserted-by":"crossref","unstructured":"Xie, C., Lai, S., Jin, L., Liao, Q. (2020). High performance offine handwritten Chinese text recognition with a new data preprocessing and augmentation pipeline. In: Proceedings of international workshop on document analysis systems, pp. 45\u201359.","DOI":"10.1007\/978-3-030-57058-3_4"},{"key":"1654_CR46","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.neucom.2019.04.001","volume":"350","author":"Z Xie","year":"2019","unstructured":"Xie, Z., Huang, Y., Jin, L., Liu, Y., Zhu, Y., Gao, L., & Zhang, X. (2019). Weakly supervised precise segmentation for historical document images. Neurocomputing, 350, 271\u2013281.","journal-title":"Neurocomputing"},{"issue":"8","key":"1654_CR47","doi-asserted-by":"publisher","first-page":"1903","DOI":"10.1109\/TPAMI.2017.2732978","volume":"40","author":"Z Xie","year":"2018","unstructured":"Xie, Z., Sun, Z., Jin, L., Ni, H., & Lyons, T. (2018). Learning spatial-semantic context with fully convolutional recurrent network for online handwritten Chinese text recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(8), 1903\u20131917.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1654_CR48","doi-asserted-by":"crossref","unstructured":"Xing, L., Tian, Z., Huang, W., Scott, M.R. (2019). Convolutional character networks. In: Proceedings of IEEE international conference on computer vision, pp. 9126\u20139136.","DOI":"10.1109\/ICCV.2019.00922"},{"key":"1654_CR49","doi-asserted-by":"crossref","unstructured":"Xiu, Y., Wang, Q., Zhan, H., Lan, M., Lu, Y. (2019). A handwritten Chinese text recognizer applying multi-level multimodal fusion network. In: Proceedings of international conference on document analysis and recognition, pp. 1464\u20131469.","DOI":"10.1109\/ICDAR.2019.00235"},{"key":"1654_CR50","doi-asserted-by":"crossref","unstructured":"Yang, H., Jin, L., Sun, J. (2018). Recognition of Chinese text in historical documents with page-level annotations. In: Proceedings of international conference on frontiers in handwriting recognition, pp. 199\u2013204.","DOI":"10.1109\/ICFHR-2018.2018.00043"},{"key":"1654_CR51","doi-asserted-by":"publisher","first-page":"30174","DOI":"10.1109\/ACCESS.2018.2840218","volume":"6","author":"H Yang","year":"2018","unstructured":"Yang, H., Jin, L., Huang, W., Yang, Z., Lai, S., & Sun, J. (2018). Dense and tight detection of Chinese characters in historical documents: Datasets and a recognition guided detector. IEEE Access, 6, 30174\u201330183.","journal-title":"IEEE Access"},{"key":"1654_CR52","doi-asserted-by":"crossref","unstructured":"Yin, F., Wang, Q., Zhang, X., Liu, C. (2013). ICDAR 2013 Chinese handwriting recognition competition. In: Proceedings of international conference on document analysis and recognition, pp. 1464\u20131470.","DOI":"10.1109\/ICDAR.2013.218"},{"key":"1654_CR53","doi-asserted-by":"crossref","unstructured":"Yousef, M., Bishop, T.E. (2020). OrigamiNet: Weakly-supervised, segmentation-free, one-step, full page text recognition by learning to unfold. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp. 14710\u201314719.","DOI":"10.1109\/CVPR42600.2020.01472"},{"key":"1654_CR54","doi-asserted-by":"crossref","unstructured":"Zhan, F., Lu, S., Xue, C. (2018). Verisimilar image synthesis for accurate detection and recognition of texts in scenes. In: Proceedings of European conference on computer vision, pp. 249\u2013266.","DOI":"10.1007\/978-3-030-01237-3_16"},{"key":"1654_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, R., Zhou, Y., Jiang, Q., Song, Q., Li, N., Zhou, K., Wang, L., Wang, D., Liao, M., Yang, M., Bai, X., Shi, B., Karatzas, D., Lu, S., Jawahar, C.V. (2019). ICDAR 2019 robust reading challenge on reading Chinese text on signboard. In: Proceedings of international conference on document analysis and recognition, pp. 1577\u20131581.","DOI":"10.1109\/ICDAR.2019.00253"},{"key":"1654_CR56","doi-asserted-by":"crossref","unstructured":"Zhang, H., Liang, L., & Jin, L. (2020). SCUT-HCCDoc: A new benchmark dataset of handwritten Chinese text in unconstrained camera-captured documents. Pattern Recognition, 108, 107559.","DOI":"10.1016\/j.patcog.2020.107559"},{"key":"1654_CR57","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yin, F., Zhang, Y., Liu, C., & Bengio, Y. (2018). Drawing and recognizing Chinese characters with recurrent neural network. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 849\u2013862.","DOI":"10.1109\/TPAMI.2017.2695539"},{"key":"1654_CR58","doi-asserted-by":"crossref","unstructured":"Zhou, X., Wang, D., Tian, F., Liu, C., & Nakagawa, M. (2013). Handwritten Chinese\/Japanese text recognition using semi-Markov conditional random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(10), 2413\u20132426.","DOI":"10.1109\/TPAMI.2013.49"},{"key":"1654_CR59","doi-asserted-by":"crossref","unstructured":"Zhu, Z.Y., Yin, F., Wang, D.H. (2020). Attention combination of sequence models for handwritten Chinese text recognition. In: Proceedings of international conference on frontiers in handwriting recognition, pp. 288\u2013294.","DOI":"10.1109\/ICFHR2020.2020.00060"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-022-01654-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-022-01654-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-022-01654-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T16:08:11Z","timestamp":1664554091000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-022-01654-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,20]]},"references-count":59,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["1654"],"URL":"https:\/\/doi.org\/10.1007\/s11263-022-01654-0","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,20]]},"assertion":[{"value":"20 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}