{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:47:58Z","timestamp":1743101278023,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030891305"},{"type":"electronic","value":"9783030891312"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-89131-2_14","type":"book-chapter","created":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T06:18:05Z","timestamp":1635574685000},"page":"153-163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Watermeter Reading in Presence of Highly Deformed Digits"],"prefix":"10.1007","author":[{"given":"Ashkan Mansouri","family":"Yarahmadi","sequence":"first","affiliation":[]},{"given":"Michael","family":"Breu\u00df","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,31]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Graves, A., Fern\u00e1ndez, S., Gomez, F.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the International Conference on Machine Learning, pp. 369\u2013376 (2006)","DOI":"10.1145\/1143844.1143891"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Ali, S., Sakhawat, Z., Mahmood, T., Aslam, M.S., Shaukat, Z., Sahiba, S.: A robust CNN model for handwritten digits recognition and classification. In: IEEE International Conference on Advances in Electrical Engineering and Computer Applications, pp. 261\u2013265 (2020)","DOI":"10.1109\/AEECA49918.2020.9213530"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., Hinton, G.: Speech Recognition with Deep Recurrent Neural Networks. arXiv:1303.5778 (2013)","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"14_CR4","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-981-10-8530-7_9","volume-title":"Internet Multimedia Computing and Service","author":"Y Gao","year":"2018","unstructured":"Gao, Y., Zhao, C., Wang, J., Lu, H.: Automatic watermeter digit recognition on mobile devices. In: Huet, B., Nie, L., Hong, R. (eds.) ICIMCS 2017. CCIS, vol. 819, pp. 87\u201395. Springer, Singapore (2018). https:\/\/doi.org\/10.1007\/978-981-10-8530-7_9"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput., 1735\u20131780 (1997)","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"14_CR7","unstructured":"Liwicki, M., Graves, A., Fern\u00e1ndez, S., Bunke, H., Schmidhuber, J.: A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks. In: Proceedings of the 9th International Conference on Document Analysis and Recognition, pp. 1\u20135 (2007)"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Kayumov, Z., Tumakov, D., Mosin, S.: Combined convolutional and perceptron neural networks for handwritten digits recognition. In: 22th International Conference on Digital Signal Processing and its Applications, pp. 1\u20135 (2020)","DOI":"10.1109\/DSPA48919.2020.9213301"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Hwang, K., Wonyong Sung, W.: Character-level incremental speech recognition with recurrent neural networks. arXiv:1601.06581 (2016)","DOI":"10.1109\/ICASSP.2016.7472696"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Ning Wang, N., Yuanyuan W., Er, M.J.: Review on deep learning techniques for marine object recognition: architectures and algorithms. Control Eng. Pract. (2020)","DOI":"10.1016\/j.conengprac.2020.104458"},{"key":"14_CR11","unstructured":"Liu, Y., Han, Y., Zhang, Y.: Image type water meter character recognition based on embedded DSP. arXiv:1508.06725 (2015)"},{"key":"14_CR12","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167 (2015)"},{"key":"14_CR13","unstructured":"LeCun, Y., Cortes, C., Burges, C.: MNIST handwritten digit database. http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"issue":"5","key":"14_CR14","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1007\/s00138-018-0942-y","volume":"29","author":"Z Lei","year":"2018","unstructured":"Lei, Z., Zhao, S., Song, H., Shen, J.: Scene text recognition using residual convolutional recurrent neural network. Mach. Vis. Appl. 29(5), 861\u2013871 (2018). https:\/\/doi.org\/10.1007\/s00138-018-0942-y","journal-title":"Mach. Vis. Appl."},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Liao, S., Zhou, P., Wang, L., Su, S.: Reading digital numbers of water meter with deep learning based object detector. Pattern Recogn. Comput. Vis., 38\u201349 (2019)","DOI":"10.1007\/978-3-030-31654-9_4"},{"key":"14_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1007\/978-3-319-70353-4_13","volume-title":"Advanced Concepts for Intelligent Vision Systems","author":"M Moniruzzaman","year":"2017","unstructured":"Moniruzzaman, M., Islam, S.M.S., Bennamoun, M., Lavery, P.: Deep learning on underwater marine object detection: a survey. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2017. LNCS, vol. 10617, pp. 150\u2013160. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-70353-4_13"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Rodriguez-Serrano, J.A., Perronnin, F., Meylan, F.: Label embedding for text recognition. In: Proceedings British Machine Vision Conference, pp. 5.1\u20135.12 (2013)","DOI":"10.5244\/C.27.5"},{"issue":"11","key":"14_CR18","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.: 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."},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Suresh, M., Muthukumar, U., Chandapillai, J.: A novel smart water-meter based on IoT and smartphone app for city distribution management. In: 2017 IEEE Region 10 Symposium (TENSYMP), pp. 1\u20135 (2017)","DOI":"10.1109\/TENCONSpring.2017.8070088"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Xiao-ping, R., Xian-feng, S.: A character recognition algorithm adapt to a specific kind of water meter. In: World Congress on Computer Science and Information Engineering, vol. 5, pp. 632\u2013636 (2009)","DOI":"10.1109\/CSIE.2009.883"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Ye, Q., Doermann, D.: Text detection and recognition in imagery: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 37 (2015)","DOI":"10.1109\/TPAMI.2014.2366765"},{"key":"14_CR22","doi-asserted-by":"publisher","first-page":"11679","DOI":"10.1109\/ACCESS.2019.2891767","volume":"7","author":"F Yang","year":"2019","unstructured":"Yang, F., Jin, L., Lai, S., Gao, X., Li, Z.: Fully convolutional sequence recognition network for water meter number reading. IEEE Access 7, 11679\u201311687 (2019)","journal-title":"IEEE Access"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Yi, L., Ni, H., Wen Z., Liu B., Tao J.: CTC regularized model adaptation for improving LSTM RNN based multi-accent Mandarin speech recognition. In: 10th International Symposium on Chinese Spoken Language Processing, pp. 1\u20135 (2016)","DOI":"10.1109\/ISCSLP.2016.7918420"},{"key":"14_CR24","unstructured":"Zeiler, M.D.: ADADELTA: an adaptive learning rate method. arXiv:1212.5701 (2012)"}],"container-title":["Lecture Notes in Computer Science","Computer Analysis of Images and Patterns"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89131-2_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T06:20:10Z","timestamp":1635574810000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89131-2_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030891305","9783030891312"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89131-2_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"31 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CAIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Analysis of Images and Patterns","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"caip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/cyprusconferences.org\/caip2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyAcademia","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"129","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":"87","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":"67% - 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","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":"4","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}