{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T07:49:16Z","timestamp":1726213756950},"reference-count":74,"publisher":"Association for Computing Machinery (ACM)","issue":"5","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2022,9,30]]},"abstract":"\n Despite the significant success of document image analysis techniques, efficient\n Optical Character Recognition (OCR)<\/jats:bold>\n of degraded document images still remains an open problem. Although a body of work has been reported on degraded document recognition for English language, only little attention has been paid to Indic scripts. In this work, we focus on developing a degraded OCR for Bangla, a major Indian language. In general, an OCR system includes segmentation of the foreground text part from the background followed by recognition of the extracted text. The text segmentation module aims to assign the foreground or background label to each pixel of the document image. In this paper, we present a new OCR system which is particularly suitable for degraded quality Bangla document images. The contribution is two fold. In the first phase, we use a semi-supervised\n Markov Random Field (MRF)-<\/jats:bold>\n based\n Generative Adversarial Network (GAN)<\/jats:bold>\n model (which we call\n \n MRF-GAN<\/jats:italic>\n <\/jats:bold>\n ) for foreground segmentation of texts from degraded text. In the proposed\n MRF-GAN<\/jats:italic>\n , we extend the concept of GAN to a multitask learning mechanism where discriminator-classifier networks differentiate between real\/fake images and also assign a foreground or background label to each pixel. In the second phase, we propose to use a new encoder-decoder based recognizer that incorporates an attention-based character to a word prediction model, which has the capability of minimizing\n Word Error Rate (WER)<\/jats:bold>\n . We optimize this network using a\n Multitask based<\/jats:bold>\n Transfer Learning scheme (MTTL)<\/jats:bold>\n . We perform experiments on a publicly available degraded Bangla document image dataset as well as on a new degraded printed Hindi document image dataset, which has been created as a part of the present study. Results of the experimentations demonstrate the efficacy of the proposed OCR.\n <\/jats:p>","DOI":"10.1145\/3511807","type":"journal-article","created":{"date-parts":[[2022,4,20]],"date-time":"2022-04-20T12:00:42Z","timestamp":1650456042000},"page":"1-20","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A Deep OCR for Degraded Bangla Documents"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-6474-5090","authenticated-orcid":false,"given":"Ayan","family":"Chaudhury","sequence":"first","affiliation":[{"name":"INRIA Grenoble Rh\u00f4ne-Alpes, France and IIT Kharagpur, West Bengal, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9254-5416","authenticated-orcid":false,"given":"Partha Sarathi","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Tatras Data, New Delhi, Delhi, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6069-0240","authenticated-orcid":false,"given":"Sudip","family":"Das","sequence":"additional","affiliation":[{"name":"Indian Statistical Institute, Kolkata, West Bengal, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4468-7396","authenticated-orcid":false,"given":"Chandan","family":"Biswas","sequence":"additional","affiliation":[{"name":"Indian Statistical Institute, Kolkata, West Bengal, India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8546-6453","authenticated-orcid":false,"given":"Ujjwal","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Indian Statistical Institute, Kolkata, India"}]}],"member":"320","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"447","volume-title":"DAS","author":"Avadesh Meduri","year":"2018","unstructured":"Meduri Avadesh and Navneet Goyal. 2018. Optical character recognition for Sanskrit using convolution neural networks. In DAS. 447\u2013452."},{"key":"e_1_3_2_3_2","first-page":"1011","volume-title":"ICDAR","author":"Chaudhuri B. B.","year":"1997","unstructured":"B. B. Chaudhuri and U. Pal. 1997. An OCR system to read two Indian language scripts: Bangla and Devnagari (Hindi). In ICDAR. 1011\u20131015."},{"key":"e_1_3_2_4_2","article-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau Dzmitry","year":"2014","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).","journal-title":"arXiv preprint arXiv:1409.0473"},{"key":"e_1_3_2_5_2","first-page":"53","volume-title":"Proc. of the 5th International Workshop on Camera-Based Document Analysis and Recognition (CBDAR 2013)","author":"Banerjee S.","year":"2013","unstructured":"S. Banerjee, K. Mullick, and U. Bhattacharya. 2013. A robust approach to extraction of texts from camera captured images. In Proc. of the 5th International Workshop on Camera-Based Document Analysis and Recognition (CBDAR 2013). 53\u201358."},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3096739"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3072900"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-012-0278-6"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2014.519"},{"key":"e_1_3_2_10_2","first-page":"3174","volume-title":"ICPR","author":"Biswas C.","year":"2018","unstructured":"C. Biswas, P. S. Mukherjee, K. Ghosh, U. Bhattacharya, and S. K. Parui. 2018. A hybrid deep architecture for robust recognition of text lines of degraded printed documents. In ICPR. 3174\u20133179."},{"key":"e_1_3_2_11_2","first-page":"428","volume-title":"ECCV","author":"Blake A.","year":"2004","unstructured":"A. Blake, C. Rother, M. A. Brown, P. P\u00e9rez, and P. H. S. Torr. 2004. Interactive image segmentation using an adaptive GMMRF Model. In ECCV. 428\u2013441."},{"key":"e_1_3_2_12_2","first-page":"105","volume-title":"ICCV","author":"Boykov Y.","year":"2001","unstructured":"Y. Boykov and M-P Jolly. 2001. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In ICCV. 105\u2013112."},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.60"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/34.969114"},{"key":"e_1_3_2_15_2","first-page":"37","article-title":"A selectional auto-encoder approach for document image binarization","volume":"86","author":"Calvo-Zaragoza J.","year":"2019","unstructured":"J. Calvo-Zaragoza and A.-J. Gallego. 2019. A selectional auto-encoder approach for document image binarization. PR 86 (2019), 37\u201347.","journal-title":"PR"},{"key":"e_1_3_2_16_2","first-page":"411","volume-title":"Proc. of 13th IAPR International Workshop on Document Analysis Systems (DAS)","author":"Chakraborty Bappaditya","year":"2018","unstructured":"Bappaditya Chakraborty, Bikash Shaw, Jayanta Aich, Ujjwal Bhattacharya, and Swapan Kumar Parui. 2018. Does deeper network lead to better accuracy: A case study on handwritten Devanagari characters. In Proc. of 13th IAPR International Workshop on Document Analysis Systems (DAS). IEEE, 411\u2013416."},{"key":"e_1_3_2_17_2","first-page":"4960","volume-title":"ICASSP","author":"Chan William","year":"2016","unstructured":"William Chan, Navdeep Jaitly, Quoc Le, and Oriol Vinyals. 2016. Listen, attend and spell: A neural network for large vocabulary conversational speech recognition. In ICASSP. 4960\u20134964."},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/978-3-642-29364-1_4","volume-title":"Camera-Based Document Analysis and Recognition, Lecture Notes in Computer Science","author":"Chowdhury A. R.","year":"2012","unstructured":"A. R. Chowdhury, U. Bhattacharya, and S. K. Parui. 2012. Text detection of two major Indian scripts in natural scene images. In Camera-Based Document Analysis and Recognition, Lecture Notes in Computer Science, Vol. 7139. 42\u201357."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3133651"},{"key":"e_1_3_2_21_2","first-page":"130","volume-title":"DAS","author":"Dutta S.","year":"2012","unstructured":"S. Dutta, N. Sankaran, K. P. Sankar, and C. V. Jawahar. 2012. Robust recognition of degraded documents using character n-grams. In DAS. 130\u2013134."},{"key":"e_1_3_2_22_2","first-page":"310","article-title":"A survey of document image word spotting techniques","volume":"68","author":"Giotis A. P.","year":"2017","unstructured":"A. P. Giotis, G. Sfikas, B. Gatos, and C. Nikou. 2017. A survey of document image word spotting techniques. PR 68 (2017), 310\u2013332.","journal-title":"PR"},{"key":"e_1_3_2_23_2","article-title":"NIPS 2016 tutorial: Generative adversarial networks","author":"Goodfellow Ian","year":"2016","unstructured":"Ian Goodfellow. 2016. NIPS 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160 (2016).","journal-title":"arXiv preprint arXiv:1701.00160"},{"key":"e_1_3_2_24_2","first-page":"2672","volume-title":"NIPS","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In NIPS. 2672\u20132680."},{"key":"e_1_3_2_25_2","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1145\/1143844.1143891","volume-title":"ICML","author":"Graves A.","year":"2006","unstructured":"A. Graves, S. Fern\u00e1ndez, F. Gomez, and J. Schmidhuber. 2006. Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. In ICML. 369\u2013376."},{"key":"e_1_3_2_26_2","first-page":"6","volume-title":"ICDAR","author":"Howe N. R.","year":"2011","unstructured":"N. R. Howe. 2011. A Laplacian energy for document binarization. In ICDAR. 6\u201310."},{"key":"e_1_3_2_27_2","first-page":"415","volume-title":"NIPS","author":"Husain Hisham","year":"2019","unstructured":"Hisham Husain, Richard Nock, and Robert C. Williamson. 2019. A primal-dual link between GANs and autoencoders. In NIPS. 415\u2013424."},{"key":"e_1_3_2_28_2","article-title":"Generating images with recurrent adversarial networks","author":"Im Daniel Jiwoong","year":"2016","unstructured":"Daniel Jiwoong Im, Chris Dongjoo Kim, Hui Jiang, and Roland Memisevic. 2016. Generating images with recurrent adversarial networks. arXiv preprint arXiv:1602.05110 (2016).","journal-title":"arXiv preprint arXiv:1602.05110"},{"key":"e_1_3_2_29_2","first-page":"1125","volume-title":"CVPR","author":"Isola Phillip","year":"2017","unstructured":"Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. 2017. Image-to-image translation with conditional adversarial networks. In CVPR. 1125\u20131134."},{"key":"e_1_3_2_30_2","first-page":"408","volume-title":"ICDAR","author":"Jawahar C. V.","year":"2003","unstructured":"C. V. Jawahar, M. N. S. S. K. Pavan Kumar, and S. S. Ravi Kiran. 2003. A bilingual OCR for Hindi-Telugu documents and its applications. In ICDAR. 408\u2013412."},{"key":"e_1_3_2_31_2","first-page":"225","article-title":"Degraded document image binarization using structural symmetry of strokes","volume":"74","author":"Jia F.","year":"2018","unstructured":"F. Jia, C. Shi, K. He, C. Wang, and B. Xiao. 2018. Degraded document image binarization using structural symmetry of strokes. PR 74 (2018), 225\u2013240.","journal-title":"PR"},{"key":"e_1_3_2_32_2","first-page":"913","volume-title":"Proc. of 7th Int. Conf. on Soft Computing for Problem Solving (SocProS)","author":"Jino P. J.","year":"2017","unstructured":"P. J. Jino, K. Balakrishnan, and U. Bhattacharya. 2017. Offline handwritten Malayalam word recognition using a deep architecture. In Proc. of 7th Int. Conf. on Soft Computing for Problem Solving (SocProS), Vol. 1. 913\u2013925."},{"key":"e_1_3_2_33_2","first-page":"1","volume-title":"Natural Language Processing and Chinese Computing. Communications in Computer and Information Science","author":"Gao C. Peng, K. Wu, Z.","year":"2013","unstructured":"C. Peng, K. Wu, Z. Gao, and X. Wen. 2013. Text window denoising autoencoder: Building deep architecture for Chinese word segmentation. In Natural Language Processing and Chinese Computing. Communications in Computer and Information Science, Vol. 400. 1\u201312."},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/0734-189X(85)90125-2"},{"key":"e_1_3_2_35_2","first-page":"748","volume-title":"ICDAR","author":"Kuk J. Gap","year":"2009","unstructured":"J. Gap Kuk and N. I. Cho. 2009. Feature based binarization of document images degraded by uneven light condition. In ICDAR. 748\u2013752."},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40010-016-0284-y"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09727-2"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-021-06060-1"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-017-9607-x"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/RICE.2018.8509076"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-018-9913-6"},{"key":"e_1_3_2_42_2","first-page":"42","volume-title":"CVPR Workshops","author":"Lahiri Avisek","year":"2017","unstructured":"Avisek Lahiri, Kumar Ayush, Prabir Kumar Biswas, and Pabitra Mitra. 2017. Generative adversarial learning for reducing manual annotation in semantic segmentation on large scale miscroscopy images: Automated vessel segmentation in retinal fundus image as test case. In CVPR Workshops. 42\u201348."},{"key":"e_1_3_2_43_2","first-page":"278","volume-title":"1st International Workshop on Document Image Analysis for Libraries","author":"Lavrenko V.","year":"2004","unstructured":"V. Lavrenko, T. M. Rath, and R. Manmatha. 2004. Holistic word recognition for handwritten historical documents. In 1st International Workshop on Document Image Analysis for Libraries. 278\u2013287."},{"key":"e_1_3_2_44_2","first-page":"2231","volume-title":"CVPR","author":"Lee Chen-Yu","year":"2016","unstructured":"Chen-Yu Lee and Simon Osindero. 2016. Recursive recurrent nets with attention modeling for OCR in the wild. In CVPR. 2231\u20132239."},{"key":"e_1_3_2_45_2","article-title":"Semantic segmentation using adversarial networks","author":"Luc Pauline","year":"2016","unstructured":"Pauline Luc, Camille Couprie, Soumith Chintala, and Jakob Verbeek. 2016. Semantic segmentation using adversarial networks. arXiv preprint arXiv:1611.08408 (2016).","journal-title":"arXiv preprint arXiv:1611.08408"},{"key":"e_1_3_2_46_2","first-page":"629","volume-title":"ICDAR","author":"Ly Nam Tuan","year":"2019","unstructured":"Nam Tuan Ly, Cuong Tuan Nguyen, and Masaki Nakagawa. 2019. An attention-based end-to-end model for multiple text lines recognition in Japanese historical documents. In ICDAR. 629\u2013634."},{"key":"e_1_3_2_47_2","first-page":"128","volume-title":"ICDAR","author":"Milyaev S.","year":"2013","unstructured":"S. Milyaev, O. Barinova, T. Novikova, P. Kohli, and V. S. Lempitsky. 2013. Image binarization for end-to-end text understanding in natural images. In ICDAR. 128\u2013132."},{"key":"e_1_3_2_48_2","first-page":"11","volume-title":"ICDAR","author":"Mishra A.","year":"2011","unstructured":"A. Mishra, K. Alahari, and C. V. Jawahar. 2011. An MRF model for binarization of natural scene text. In ICDAR. 11\u201316."},{"key":"e_1_3_2_49_2","first-page":"1","volume-title":"Proc. of ICAPR","author":"Mullick K.","year":"2015","unstructured":"K. Mullick, S. Banerjee, and U. Bhattacharya. 2015. An efficient line segmentation approach for handwritten Bangla document image. In Proc. of ICAPR. 1\u20136."},{"key":"e_1_3_2_50_2","first-page":"1","article-title":"UrduDeepNet: Offline handwritten Urdu character recognition using deep neural network","author":"Mushtaq F.","year":"2021","unstructured":"F. Mushtaq, M. M. Misgar, M. Kumar, and S. S. Khurana. 2021. UrduDeepNet: Offline handwritten Urdu character recognition using deep neural network. Neural Computing and Applications (2021), 1\u201324.","journal-title":"Neural Computing and Applications"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2322451"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12046-019-1126-9"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-05018-z"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-10775-6"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2015.10.017"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.5555\/4901"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1979.4310076"},{"key":"e_1_3_2_58_2","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1145\/1924559.1924569","volume-title":"ICVGIP","author":"Peng Xujun","year":"2010","unstructured":"Xujun Peng, Srirangaraj Setlur, Venu Govindaraju, and Ramachandrula Sitaram. 2010. Markov random field based binarization for hand-held devices captured document images. In ICVGIP. 71\u201376."},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/2505377.2505384"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/1015706.1015720"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10032-015-0254-y"},{"issue":"2","key":"e_1_3_2_62_2","first-page":"225","article-title":"Adaptive document image binarization","volume":"33","author":"Sauvola J. J.","year":"2000","unstructured":"J. J. Sauvola and M. Pietik\u00e4inen. 2000. Adaptive document image binarization. PR 33, 2 (2000), 225\u2013236.","journal-title":"PR"},{"key":"e_1_3_2_63_2","first-page":"720","volume-title":"Proc. of ACPR","author":"Shaw B.","year":"2015","unstructured":"B. Shaw, U. Bhattacharya, and S. K. Parui. 2015. Offline handwritten Devanagari word recognition : Information fusion at feature and classifier levels. In Proc. of ACPR. IEEE, 720\u2013724."},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-021-05620-9"},{"key":"e_1_3_2_65_2","first-page":"5688","volume-title":"ICCV","author":"Souly Nasim","year":"2017","unstructured":"Nasim Souly, Concetto Spampinato, and Mubarak Shah. 2017. Semi supervised semantic segmentation using generative adversarial network. In ICCV. 5688\u20135696."},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2231089"},{"key":"e_1_3_2_67_2","first-page":"25","volume-title":"DAS","author":"Tang Y.","year":"2016","unstructured":"Y. Tang, L. Peng, Q. Xu, Y. Wang, and A. Furuhata. 2016. CNN based transfer learning for historical Chinese character recognition. In DAS. 25\u201329."},{"key":"e_1_3_2_68_2","first-page":"99","volume-title":"ICDAR","author":"Tensmeyer C.","year":"2017","unstructured":"C. Tensmeyer and T. Martinez. 2017. Document image binarization with fully convolutional neural networks. In ICDAR. 99\u2013104."},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2015.07.009"},{"key":"e_1_3_2_70_2","article-title":"Wasserstein auto-encoders","author":"Tolstikhin Ilya","year":"2017","unstructured":"Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf. 2017. Wasserstein auto-encoders. arXiv preprint arXiv:1711.01558 (2017).","journal-title":"arXiv preprint arXiv:1711.01558"},{"key":"e_1_3_2_71_2","first-page":"568","article-title":"Binarization of degraded document images based on hierarchical deep supervised network","volume":"74","author":"Vo Q. N.","year":"2018","unstructured":"Q. N. Vo, S.-H. Kim, H. J. Yang, and G. Lee. 2018. Binarization of degraded document images based on hierarchical deep supervised network. PR 74 (2018), 568\u2013586.","journal-title":"PR"},{"key":"e_1_3_2_72_2","first-page":"334","volume-title":"NIPS","author":"Wang Jianfeng","year":"2017","unstructured":"Jianfeng Wang and Xiaolin Hu. 2017. Gated recurrent convolution neural network for OCR. In NIPS. 334\u2013343."},{"key":"e_1_3_2_73_2","first-page":"160","volume-title":"ICPR","author":"Wolf C.","year":"2002","unstructured":"C. Wolf and D. S. Doermann. 2002. Binarization of low quality text using a Markov random field model. In ICPR. 160\u2013163."},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2017.8122728"},{"key":"e_1_3_2_75_2","article-title":"On the discrimination-generalization tradeoff in GANs","author":"Zhang Pengchuan","year":"2017","unstructured":"Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, and Xiaodong He. 2017. On the discrimination-generalization tradeoff in GANs. arXiv preprint arXiv:1711.02771 (2017).","journal-title":"arXiv preprint arXiv:1711.02771"}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511807","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T01:07:31Z","timestamp":1672621651000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511807"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,25]]},"references-count":74,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,9,30]]}},"alternative-id":["10.1145\/3511807"],"URL":"https:\/\/doi.org\/10.1145\/3511807","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"value":"2375-4699","type":"print"},{"value":"2375-4702","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,25]]},"assertion":[{"value":"2021-07-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-01-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-08-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}