{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T14:58:52Z","timestamp":1758121132881},"reference-count":35,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2023,11,1]]},"DOI":"10.1587\/transfun.2023smp0007","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T12:25:05Z","timestamp":1684153505000},"page":"1406-1415","source":"Crossref","is-referenced-by-count":3,"title":["U-Net Architecture for Ancient Handwritten Chinese Character Detection in Han Dynasty Wooden Slips"],"prefix":"10.1587","volume":"E106.A","author":[{"given":"Hojun","family":"SHIMOYAMA","sequence":"first","affiliation":[{"name":"Faculty of Engineering Science, Kansai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soh","family":"YOSHIDA","sequence":"additional","affiliation":[{"name":"Faculty of Engineering Science, Kansai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takao","family":"FUJITA","sequence":"additional","affiliation":[{"name":"Faculty of Letters, Kansai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mitsuji","family":"MUNEYASU","sequence":"additional","affiliation":[{"name":"Faculty of Engineering Science, Kansai University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] T. Fujita, \u201cA basic consideration for the handwrighting analysis of the han woodslips,\u201d Essays on the Occation of the 70th Anniversary of the Institute of Oriental and Occidental Studies, Kansai University, pp.357-376, 2013."},{"key":"2","unstructured":"[2] T. Sumiya, \u201cStudies on the wooded strips during the qin and han dynasties,\u201d The Journal of Oriental Researches, vol.55, no.1, 1996."},{"key":"3","unstructured":"[3] M. Loewe, Records of Han Administration, Cambridge Univ. Press, 1967."},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, \u201cGradient-based learning applied to document recognition,\u201d Proc. IEEE, vol.86, no.11, pp.2278-2324, 1998. 10.1109\/5.726791","DOI":"10.1109\/5.726791"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] S. Capobianco, L. Scommegna, and S. Marinai, \u201cHistorical handwritten document segmentation by using a weighted loss,\u201d Proc. Artif. Neural Netw. Pattern Recognit. (ANNPR), pp.395-406, 2018. 10.1007\/978-3-319-99978-4_31","DOI":"10.1007\/978-3-319-99978-4_31"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] S. Long, J. Ruan, W. Zhang, X. He, W. Wu, and C. Yao, \u201cTextSnake: A flexible representation for detecting text of arbitrary shapes,\u201d Proc. Eur. Conf. Comp. Vis. (ECCV), pp.19-35, 2018. 10.1007\/978-3-030-01216-8_2","DOI":"10.1007\/978-3-030-01216-8_2"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] Y. Baek, B. Lee, D. Han, S. Yun, and H. Lee, \u201cCharacter region awareness for text detection,\u201d Proc. Comput. Vis. Pattern Recognit. Conf. (CVPR), pp.9365-9374, 2019. 10.1109\/cvpr.2019.00959","DOI":"10.1109\/CVPR.2019.00959"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] D. Valy, M. Verleysen, and S. Chhun, \u201cData augmentation and text recognition on Khmer historical manuscripts,\u201d Proc. Int. Conf. Front. Handwrit. Recog. (ICFHR), pp.73-78, 2020. 10.1109\/icfhr2020.2020.00024","DOI":"10.1109\/ICFHR2020.2020.00024"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] Z. Ziran, X. Pic, S. Undri Innocenti, D. Mugnai, and S. Marinai, \u201cText alignment in early printed books combining deep learning and dynamic programming,\u201d Pattern Recognit. Lett., vol.133, pp.109-115, 2020. 10.1016\/j.patrec.2020.02.016","DOI":"10.1016\/j.patrec.2020.02.016"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] A. Droby, B.K. Barakat, B. Madi, R. Alaasam, and J. El-Sana, \u201cUnsupervised deep learning for handwritten page segmentation,\u201d Proc. Int. Conf. Front. Handwrit. Recog. (ICFHR), pp.240-245, 2020. 10.1109\/icfhr2020.2020.00052","DOI":"10.1109\/ICFHR2020.2020.00052"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] J. Ye, Z. Chen, J. Liu, and B. Du, \u201cTextFuseNet: Scene text detection with richer fused features,\u201d Proc. Int. Joint Conf. Artif. Intell. (IJCAI), pp.516-522, 2020. 10.24963\/ijcai.2020\/72","DOI":"10.24963\/ijcai.2020\/72"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] C.W. Tang, C.L. Liu, P.S. Chu, C.L. Liu, and P.S. Chiu, \u201cHRCenterNet: An anchorless approach to chinese character segmentation in historical documents,\u201d Proc. Int. Conf. Big Data (Big Data), pp.1924-1930, 2020. 10.1109\/bigdata50022.2020.9378051","DOI":"10.1109\/BigData50022.2020.9378051"},{"key":"13","unstructured":"[13] H. Shimoyama, S. Yoshida, T. Fujita, and M. Muneyasu, \u201cCharacter region detection from wood slips of the Chinese Han period focusing on character boundaries based on U-Net,\u201d IEICE Technical Report, SIS2021-35, 2022."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] R. Girshick, \u201cFast R-CNN,\u201d Proc. Int. Conf. Comput. Vis. (ICCV), pp.1440-1448, 2015. 10.1109\/iccv.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"key":"15","unstructured":"[15] S. Ren, K. He, R. Girshick, and J. Sun, \u201cFaster R-CNN: Towards real-time object detection with region proposal networks,\u201d Proc. Conf. Neural Inf. Process. Syst. (NeurIPS), pp.91-99, 2015."},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, \u201cYou only look once: Unified, real-time object detection,\u201d Proc. Comput. Vis. Pattern Recognit. Conf. (CVPR), pp.779-788, 2016. 10.1109\/cvpr.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] J. Long, E. Shelhamer, and T. Darrell, \u201cFully convolutional networks for semantic segmentation,\u201d Proc. Comput. Vis. Pattern Recognit. Conf. (CVPR), pp.3431-3440, 2015. 10.1109\/cvpr.2015.7298965","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] O. Ronneberger, P. Fischer, and T. Brox, \u201cU-Net: Convolutional networks for biomedical image segmentation,\u201d Int. Conf. Med. Image Comput. Comput. Assist. Interv. (MICCAI), pp.234-241, 2015. 10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A.L. Yuille, \u201cDeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.40, no.4, pp.834-848, 2018. 10.1109\/tpami.2017.2699184","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] G. Lin, A. Milan, C. Shen, and I. Reid, \u201cRefineNet: Multi-path refinement networks for high-resolution semantic segmentation,\u201d Proc. Comput. Vis. Pattern Recognit. Conf. (CVPR), 2017. 10.1109\/cvpr.2017.549","DOI":"10.1109\/CVPR.2017.549"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] K. He, G. Gkioxari, P. Dollar, and R. Girshick, \u201cMask R-CNN,\u201d Proc. Int. Conf. Comput. Vis. (ICCV), pp.2961-2969, 2017. 10.1109\/iccv.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] H. Law and J. Deng, \u201cCornerNet: Detecting objects as paired keypoints,\u201d Int. J. Comput. Vis., vol.128, no.3, pp.642-656, 2020. 10.1007\/s11263-019-01204-1","DOI":"10.1007\/s11263-019-01204-1"},{"key":"23","unstructured":"[23] K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d Proc. Int. Conf. Learn. Represent. (ICLR), pp.1-14, 2019."},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] E. Arkin, N. Yadikar, X. Xu, A. Aysa, and K. Ubul, \u201cA survey: Object detection methods from CNN to transformer,\u201d Multimed. Tools Appl., vol.82, no.14, pp.21353-21383, 2023. 10.1007\/s11042-022-13801-3","DOI":"10.1007\/s11042-022-13801-3"},{"key":"25","unstructured":"[25] X. Zhou, D. Wang, and P. Kr\u00e4henb\u00fchl, \u201cObjects as points,\u201d arXiv preprint arXiv:1904.07850, 2019. 10.48550\/arXiv.1904.07850"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] D. Karatzas, F. Shafait, S. Uchida, M. Iwamura, L.G.i. Bigorda, S.R. Mestre, J. Mas, D.F. Mota, J.A. Almaz\u00e0n, and L.P. de las Heras, \u201cICDAR 2013 robust reading competition,\u201d Proc. Int. Conf. Doc. Anal. Recognit. (ICDAR), pp.1484-1493, 2013. 10.1109\/icdar.2013.221","DOI":"10.1109\/ICDAR.2013.221"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] L. He, X. Ren, Q. Gao, X. Zhao, B. Yao, and Y. Chao, \u201cThe connected-component labeling problem: A review of state-of-the-art algorithms,\u201d Pattern Recognit., vol.70, pp.25-43, 2017. 10.1016\/j.patcog.2017.04.018","DOI":"10.1016\/j.patcog.2017.04.018"},{"key":"28","unstructured":"[28] G. Bradski and A. Kaehler, Learning OpenCV: Computer Vision With the OpenCV Library, O'Reilly Media, Farnham, U.K., 2008."},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] N. Bodla, B. Singh, R. Chellappa, and L.S. Davis, \u201cSoft-NMS \u2014 Improving object detection with one line of code,\u201d Proc. Int. Conf. Comput. Vis. (ICCV), pp.5562-5570, 2017. 10.1109\/iccv.2017.593","DOI":"10.1109\/ICCV.2017.593"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] Y. Xu, F. Yin, D.H. Wang, X.Y. Zhang, Z. Zhang, and C.L. Liu, \u201cCASIA-AHCDB: A large-scale chinese ancient handwritten characters database,\u201d Proc. Int. Conf. Doc. Anal. Recognit. (ICDAR), pp.793-798, 2019. 10.1109\/icdar.2019.00132","DOI":"10.1109\/ICDAR.2019.00132"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] H. Yang, L. Jin, W. Huang, Z. Yang, S. Lai, and J. Sun, \u201cDense and tight detection of Chinese characters in historical documents: Datasets and a recognition guided detector,\u201d IEEE Access, vol.6, pp.30174-30183, 2018. 10.1109\/access.2018.2840218","DOI":"10.1109\/ACCESS.2018.2840218"},{"key":"32","unstructured":"[32] edited by \u7c21\u7258\u6574\u7406\u5c0f\u7d44, The documents of the Han dynasty on wooden slips from Edsen-gol \u5c45\u5ef6\u6f22\u7c21, Institute of History and Philology, Academia Sinica, 2014."},{"key":"33","doi-asserted-by":"publisher","unstructured":"[33] L. Jiao, F. Zhang, F. Liu, S. Yang, L. Li, Z. Feng, and R. Qu, \u201cA survey of deep learning-based object detection,\u201d IEEE Access, vol.7, pp.128837-128868, 2019. 10.1109\/access.2019.2939201","DOI":"10.1109\/ACCESS.2019.2939201"},{"key":"34","doi-asserted-by":"publisher","unstructured":"[34] T. Khan, R. Sarkar, and A.F. Mollah, \u201cDeep learning approaches to scene text detection: A comprehensive review,\u201d Artif. Intell. Rev., vol.54, no.5, p.3239-3298, 2021. 10.1007\/s10462-020-09930-6","DOI":"10.1007\/s10462-020-09930-6"},{"key":"35","unstructured":"[35] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, \u201cPyTorch: An imperative style, high-performance deep learning library,\u201d Proc. Conf. Neural Inf. Process. Syst. (NeurIPS), pp.8024-8035, 2019."}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E106.A\/11\/E106.A_2023SMP0007\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T03:18:29Z","timestamp":1699067909000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E106.A\/11\/E106.A_2023SMP0007\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,1]]},"references-count":35,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2023smp0007","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"value":"0916-8508","type":"print"},{"value":"1745-1337","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,1]]},"article-number":"2023SMP0007"}}