{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:23:10Z","timestamp":1773487390071,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2018,8,29]],"date-time":"2018-08-29T00:00:00Z","timestamp":1535500800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Beijing Technology Plan Project","award":["Z171100002217094"],"award-info":[{"award-number":["Z171100002217094"]}]},{"name":"National Defense Science and Technology Project","award":["17-163-12-XJ-003-003-01"],"award-info":[{"award-number":["17-163-12-XJ-003-003-01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["IJDAR"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1007\/s10032-018-0311-4","type":"journal-article","created":{"date-parts":[[2018,8,30]],"date-time":"2018-08-30T01:08:13Z","timestamp":1535591293000},"page":"233-240","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Building efficient CNN architecture for offline handwritten Chinese character recognition"],"prefix":"10.1007","volume":"21","author":[{"given":"Zhiyuan","family":"Li","sequence":"first","affiliation":[]},{"given":"Nanjun","family":"Teng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4648-3215","authenticated-orcid":false,"given":"Min","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Huaxiang","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,8,29]]},"reference":[{"issue":"1","key":"311_CR1","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1109\/TPAMI.1987.4767881","volume":"9","author":"F Kimura","year":"1987","unstructured":"Kimura, F., Takashina, K., Tsuruoka, S., Miyake, Y.: Modified quadratic discriminant functions and the application to Chinese character recognition. IEEE Trans. Pattern Anal. Mach. Intell. 9(1), 149\u2013153 (1987)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"311_CR2","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and $$<0.5$$ < 0.5 mb model size. In: Computer Vision and Pattern Recognition (2016). arXiv:1602.07360"},{"issue":"1","key":"311_CR3","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.patcog.2012.06.021","volume":"46","author":"C Liu","year":"2013","unstructured":"Liu, C., Yin, F., Wang, D., Wang, Q.: Online and offline handwritten Chinese character recognition: benchmarking on new databases. Pattern Recognit. 46(1), 155\u2013162 (2013)","journal-title":"Pattern Recognit."},{"key":"311_CR4","unstructured":"Yong, G., Qiang, H., Zhidan, F.: Chinese character recognition: history, status, and prospects. In: Proceedings of the 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, FL, USA. IEEE (2002)"},{"issue":"8","key":"311_CR5","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.1109\/TPAMI.2007.1090","volume":"29","author":"C Liu","year":"2007","unstructured":"Liu, C.: 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":"311_CR6","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/978-1-4757-3279-5_11","volume-title":"Complementarity: Applications, Algorithms and Extensions","author":"OL Mangasarian","year":"2001","unstructured":"Mangasarian, O.L., Musicant, D.R.: Data discrimination via nonlinear generalized support vector machines. In: Ferris, M.C., Mangasarian, O.L., Pang, J.S. (eds.) Complementarity: Applications, Algorithms and Extensions, pp. 233\u2013251. Springer, Boston (2001)"},{"issue":"2","key":"311_CR7","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1109\/TNN.2004.824263","volume":"15","author":"C Liu","year":"2004","unstructured":"Liu, C., Sako, H., Fujisawa, H.: Discriminative learning quadratic discriminant function for handwriting recognition. IEEE Trans. Neural Netw. 15(2), 430\u2013444 (2004)","journal-title":"IEEE Trans. Neural Netw."},{"key":"311_CR8","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Neural. Inf. Proc. Syst. 1097\u20131105 (2012)"},{"key":"311_CR9","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)"},{"key":"311_CR10","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"311_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"311_CR12","doi-asserted-by":"crossref","unstructured":"Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: Computer Vision and Pattern Recognition, pp. 3642\u20133649 (2012)","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"311_CR13","doi-asserted-by":"crossref","unstructured":"Ciresan, D.C., Meier, U.: Multi-column deep neural networks for offline handwritten Chinese character classification. In: International Symposium on Neural Networks, pp. 1\u20136 (2015)","DOI":"10.1109\/IJCNN.2015.7280516"},{"key":"311_CR14","doi-asserted-by":"crossref","unstructured":"Yin, F., Wang, Q., Zhang, X., Liu, C.: ICDAR 2013 Chinese handwriting recognition competition. In: Proceedings of International Conference on Document Analysis and Recognition (ICDAR), pp. 1095\u20131101 (2013)","DOI":"10.1109\/ICDAR.2013.218"},{"key":"311_CR15","doi-asserted-by":"crossref","unstructured":"Wu, C., Fan, W., He, Y., Sun, J., Naoi, S.: Handwritten character recognition by alternately trained relaxation convolutional neural network. Int. Conf. Front. Handwriting. Recogn. 291\u2013296 (2014)","DOI":"10.1109\/ICFHR.2014.56"},{"key":"311_CR16","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten Chinese character recognition using googlenet and directional feature maps. In: International Conference on Document Analysis and Recognition, pp. 846\u2013850 (2015)","DOI":"10.1109\/ICDAR.2015.7333881"},{"key":"311_CR17","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.patcog.2016.08.005","volume":"61","author":"X Zhang","year":"2017","unstructured":"Zhang, X., Bengio, Y., Liu, C.: Online and offline handwritten Chinese character recognition: a comprehensive study and new benchmark. Pattern Recognit. 61, 348\u2013360 (2017)","journal-title":"Pattern Recognit."},{"key":"311_CR18","unstructured":"Chen, W., Wilson, J.T., Tyree, S., Weinberger, K.Q., Chen, Y.: Compressing neural networks with the hashing trick. In: International Conference on Machine Learning, pp. 2285\u20132294 (2015)"},{"key":"311_CR19","doi-asserted-by":"crossref","unstructured":"Xue, J., Li, J., Gong, Y.: Restructuring of deep neural network acoustic models with singular value decomposition. Conf. Int. Speech. Commun. Assoc. 2365\u20132369 (2013)","DOI":"10.21437\/Interspeech.2013-552"},{"key":"311_CR20","doi-asserted-by":"crossref","unstructured":"Jaderberg, M., Vedaldi, A., Zisserman, A.: Speeding up convolutional neural networks with low rank expansions. In: British Machine Vision Conference (2014)","DOI":"10.5244\/C.28.88"},{"key":"311_CR21","unstructured":"Lebedev, V., Ganin, Y., Rakhuba, M., Oseledets, I.V., Lempitsky, V.S.: Speeding-up convolutional neural networks using fine-tuned cp-decomposition. In: International Conference on Learning Representations (2015)"},{"key":"311_CR22","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. In: International Conference on Learning Representations (2016)"},{"key":"311_CR23","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. In: Computer Vision and Pattern Recognition (2016). arXiv:1608.08710"},{"key":"311_CR24","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks. In: The IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.155"},{"key":"311_CR25","unstructured":"Lin, M., Chen, Q., Yan, S.: Network in network. In: International Conference on Learning Representations (2014)"},{"key":"311_CR26","unstructured":"Andrew, H.G., Menglong, Z., Bo, C., Dmitry, K., Weijun, W., Tobias, W., Marco, A., Adam, H.: Mobilenets: efficient convolutional neural networks for mobile vision applications. In: Computer Vision and Pattern Recognition (2017). arXiv:1704.04861"},{"key":"311_CR27","unstructured":"Xiangyu, Z., Xinyu, Z., Mengxiao, L., Jian, S.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Computer Vision and Pattern Recognition (2017). arXiv:1707.01083"},{"key":"311_CR28","unstructured":"Courbariaux, M., Hubara, I., Soudry, D., Elyaniv, R., Bengio, Y.: Binarized neural networks: training deep neural networks with weights and activations constrained to $$+1$$ + 1 or $$-1$$ - 1 . In: Learning (2016). arXiv:1602.02830"},{"key":"311_CR29","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-319-46493-0_32","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Mohammad Rastegari","year":"2016","unstructured":"Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: Xnor-net: imagenet classification using binary convolutional neural networks. In: European Conference on Computer Vision, pp. 525\u2013542 (2016)"},{"key":"311_CR30","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.patcog.2017.06.032","volume":"72","author":"X Xiao","year":"2017","unstructured":"Xiao, X., Jin, L., Yang, Y., Yang, W., Sun, J., Chang, T.: Building fast and compact convolutional neural networks for offline handwritten Chinese character recognition. Pattern Recognit. 72, 72\u201381 (2017)","journal-title":"Pattern Recognit."},{"key":"311_CR31","doi-asserted-by":"crossref","unstructured":"Liu, C., Yin, F., Wang, D., Wang, Q.: CASIA online and offline Chinese handwriting databases. Int. Conf. Doc. Anal. Recogn. 37\u201341 (2011)","DOI":"10.1109\/ICDAR.2011.17"},{"key":"311_CR32","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al.: Tensorflow: a system for large-scale machine learning. In: Operating Systems Design and Implementation, pp. 265\u2013283 (2016)"}],"container-title":["International Journal on Document Analysis and Recognition (IJDAR)"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10032-018-0311-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10032-018-0311-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10032-018-0311-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T21:07:22Z","timestamp":1661893642000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10032-018-0311-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,29]]},"references-count":32,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["311"],"URL":"https:\/\/doi.org\/10.1007\/s10032-018-0311-4","relation":{},"ISSN":["1433-2833","1433-2825"],"issn-type":[{"value":"1433-2833","type":"print"},{"value":"1433-2825","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,29]]},"assertion":[{"value":"11 April 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2018","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}