{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:57:05Z","timestamp":1765357025857,"version":"3.41.0"},"reference-count":148,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T00:00:00Z","timestamp":1635897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2022,3,31]]},"abstract":"<jats:p>\n            Handwritten script classification is still considered as a challenging research problem in the domain of document image analysis. Although some research attempts have been made by the researchers for solving the challenging issues, a comprehensive solution is yet to be achieved. The case study, undertaken here, analyzes the performances of various state-of-the art handwritten script classification methods for Indian scripts where features, needed for the script classification task, are extracted from the script images at four different granularity levels, i.e., page, block, text line, or word. The results of handwritten script classification at each level have been obtained and compared using eight different feature sets and six different state-of-the-art classifiers. Based on the classification results, an ideal level for performing the handwritten script classification task is suggested among these four classification levels. The results have also been improved by using two feature dimensionality reduction methods. All these experiments are done on two different handwritten\n            <jats:italic>Indic<\/jats:italic>\n            script databases, of which one is an in-house developed dataset and the other one is a freely available dataset. Finally, some future research directions that may be undertaken by the researchers as an application of the handwritten\n            <jats:italic>Indic<\/jats:italic>\n            script classification problem are also highlighted. The work presented here provides a basic foundation for the construction of a comprehensive handwritten script classification method for official Indian scripts.\n          <\/jats:p>","DOI":"10.1145\/3476102","type":"journal-article","created":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T15:03:32Z","timestamp":1635951812000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A Case Study on Handwritten\n            <i>Indic<\/i>\n            Script Classification: Benchmarking of the Results at Page, Block, Text-line, and Word Levels"],"prefix":"10.1145","volume":"21","author":[{"given":"Pawan Kumar","family":"Singh","sequence":"first","affiliation":[{"name":"Department of Information Technology, Jadavpur University, Kolkata, West Bengal, INDIA"}]},{"given":"Ram","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, INDIA"}]},{"given":"Ajith","family":"Abraham","sequence":"additional","affiliation":[{"name":"Machine Intelligence Research (MIR) Labs, Scientific Network for Innovation and Research Excellence, Auburn, Washington, USA and Center for Artificial Intelligence, Innopolis University, Russia"}]},{"given":"Mita","family":"Nasipuri","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, INDIA"}]}],"member":"320","published-online":{"date-parts":[[2021,11,3]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.30"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001418560128"},{"key":"e_1_3_2_4_2","article-title":"A survey of mono- and multi-lingual character recognition using deep and shallow architectures: Indic and non-Indic scripts","author":"Kaur S.","year":"2019","unstructured":"S. Kaur, S. Bawa, and R. Kumar. 2019. A survey of mono- and multi-lingual character recognition using deep and shallow architectures: Indic and non-Indic scripts. Artif. Intell. Rev. 53 (2019), 1813\u20131872. DOI:https:\/\/doi.org\/10.1007\/s10462-019-09720-9","journal-title":"Artif. Intell. Rev."},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.22452\/mjcs.vol31no1.5"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2013.81"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-11758-4_39"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2015.7333847"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1158"},{"key":"e_1_3_2_10_2","first-page":"1","article-title":"Character and numeral recognition for non-Indic and Indic scripts: A survey","author":"Kumar M.","year":"2018","unstructured":"M. Kumar, M. Jindal, R. Sharma, and S. R. Jindal. 2018. Character and numeral recognition for non-Indic and Indic scripts: A survey. Artif. Intell. Rev. 4 (2018), 1\u201327.","journal-title":"Artif. Intell. Rev."},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-015-1972-2"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2015.7333921"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.22452\/mjcs.vol28no4.2"},{"key":"e_1_3_2_14_2","unstructured":"Retrieved on 12 April 2019 from http:\/\/rajbhasha.nic.in\/hi\/languages_included_in_the_eighth_schedule."},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3126650"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2014.12.001"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13735-017-0130-2"},{"key":"e_1_3_2_18_2","first-page":"6546","article-title":"Script identification of multi-script documents: A survey","volume":"5","author":"Ubul K.","year":"2017","unstructured":"K. Ubul, G. Tursun, A. Aysa, D. Impedovo, G. Pirlo, and T. Yibulayin. 2017. Script identification of multi-script documents: A survey. In IEEE Access 5 (2017) 6546\u20136559. DOI:10.1109\/ACCESS.2017.2689159","journal-title":"IEEE Access"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-016-0555-x"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-45062-4_70"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2015.7333932"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001417530032"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-017-0702-8"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.27.5.051214"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2015.7333932"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-81-322-2247-7_30"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/C3IT.2015.7060113"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1002\/9781119242963.ch3"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/ReTIS.2015.7232882"},{"key":"e_1_3_2_30_2","first-page":"139","volume-title":"3rd International Doctoral Symposium on Applied Computation and Security Systems (ACSS)","author":"Singh P. K.","year":"2016","unstructured":"P. K. Singh, R. Sarkar, and M. Nasipuri. 2016. Statistical textural features for text-line level handwritten Indic script identification. In 3rd International Doctoral Symposium on Applied Computation and Security Systems (ACSS). 139\u2013151."},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.4018\/IJCVIP.2016070102"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2013.76"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICFHR.2014.69"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/ReTIS.2015.7232880"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.4018\/IJCVIP.2017040106"},{"issue":"1","key":"e_1_3_2_36_2","first-page":"256","article-title":"Script recognition using GLCM and DWT features","volume":"4","author":"Vijayalaxmi M. B.","year":"2015","unstructured":"M. B. Vijayalaxmi and B. V. Dhandra. 2015. Script recognition using GLCM and DWT features. Int. J. Adv. Res. Comput. Commun. Eng. 4, 1 (2015), 256\u2013260.","journal-title":"Int. J. Adv. Res. Comput. Commun. Eng."},{"key":"e_1_3_2_37_2","volume-title":"Pattern Recognition Techniques, Technology and Applications","author":"Mingqiang Y.","year":"2008","unstructured":"Y. Mingqiang, K. Kidiyo, and R. Joseph. 2008. A survey of shape feature extraction techniques. In Pattern Recognition Techniques, Technology and Applications, Peng-Yeng Yin (Ed.). pp. 626."},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.177"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.5555\/573607"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1979.4310076"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1364\/JOSAA.2.001160"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/21.44046"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0146-664X(75)80008-6"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/0167-8655(90)90112-F"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/0167-8655(91)80014-2"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-11164-8_60"},{"key":"e_1_3_2_48_2","first-page":"517","volume-title":"1st International Conference on Intelligent Computing and Communication (ICIC2)","author":"Singh P. K.","year":"2016","unstructured":"P. K. Singh, S. P. Chowdhury, S. Sinha, S. Eum, and R. Sarkar. 2016. Page-to-word extraction from unconstrained handwritten document images. In 1st International Conference on Intelligent Computing and Communication (ICIC2). 517\u2013524."},{"key":"e_1_3_2_49_2","volume-title":"IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence","author":"Rish I.","year":"2001","unstructured":"I. Rish. 2001. An empirical study of the naive Bayes classifier. In IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence."},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022627411411"},{"issue":"6","key":"e_1_3_2_51_2","first-page":"1245","article-title":"Comparison of the multilayer perceptron and the nearest neighbor classifier for handwritten digit recognition","volume":"21","author":"Roy K.","year":"2005","unstructured":"K. Roy, C. Chaudhuri, M. Kundu, M. Nasipuri, and D. K. Basu. 2005. Comparison of the multilayer perceptron and the nearest neighbor classifier for handwritten digit recognition. J. Inf. Sci. Eng. 21, 6 (2005), 1245\u20131257.","journal-title":"J. Inf. Sci. Eng."},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007614523901"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.5555\/844379.844681"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1532-0464(03)00034-0"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-1052-4"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1504\/IJAPR.2015.068929"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1937.10503522"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177731944"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSEM.2010.14"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1515\/jisys-2016-0070"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-017-4373-y"},{"issue":"1","key":"e_1_3_2_62_2","first-page":"1","article-title":"A comprehensive survey of handwritten document benchmarks: Structure, usage and evaluation","volume":"5","author":"Hussain R.","year":"2015","unstructured":"R. Hussain, A. Raza, I. Siddiqi, K. Khurshid, and C. Djeddi. 2015. A comprehensive survey of handwritten document benchmarks: Structure, usage and evaluation. EURASIP J. Image Video Proces. 5, 1 (2015) 46, 1\u201324.","journal-title":"EURASIP J. Image Video Proces."},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2012.04.004"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2014.04.006"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2017.04.011"},{"key":"e_1_3_2_66_2","first-page":"82","volume-title":"International Conference on Computational Intelligence, Communications, and Business Analytics (CICBA)","author":"Sarkar R.","year":"2018","unstructured":"R. Sarkar, M. Ghosh, A. Chatterjee, and S. Malakar. 2018. An advanced particle swarm optimization based feature selection method for tri-script handwritten digit recognition. In International Conference on Computational Intelligence, Communications, and Business Analytics (CICBA). 82\u201394."},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2017.12.014"},{"issue":"4","key":"e_1_3_2_68_2","first-page":"20","article-title":"Feature selection for Bangla handwritten word recognition using harmony search","volume":"10","author":"Singh P. K.","year":"2016","unstructured":"P. K. Singh, S. Das, S. Bhowmik, R. Sarkar, and M. Nasipuri. 2016. Feature selection for Bangla handwritten word recognition using harmony search. Int. J. Inf. Process. 10, 4 (2016), 20\u201333.","journal-title":"Int. J. Inf. Process."},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.06.057"},{"issue":"6","key":"e_1_3_2_70_2","article-title":"A clustering-based feature selection framework for handwritten Indic script classification","volume":"36","author":"Chatterjee I.","year":"2019","unstructured":"I. Chatterjee, M. Ghosh, P. K. Singh, R. Sarkar, and M. Nasipuri. 2019. A clustering-based feature selection framework for handwritten Indic script classification. Exp. Syst. 36, 6 (2019). DOI:https:\/\/doi.org\/10.1111\/exsy.12459","journal-title":"Exp. Syst."},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2354694"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2017.10.001"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.04.010"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.01.008"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2016.01.032"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2017.10.001"},{"key":"e_1_3_2_77_2","article-title":"Deep learning for word level handwritten Indic script identification","author":"Ukil S.","year":"2018","unstructured":"S. Ukil, S. Ghosh, Sk. Md. Obaidullah, K. C. Santosh, K. Roy, and N. Das. 2018. Deep learning for word level handwritten Indic script identification. CoRR abs\/1801.01627 (2018).","journal-title":"CoRR"},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04111-1"},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04235-4"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2014.09.003"},{"key":"e_1_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1109\/SITIS.2015.15"},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.07.034"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2013.177"},{"key":"e_1_3_2_84_2","first-page":"225","volume-title":"3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA)","author":"Gaikwad A. P.","year":"2014","unstructured":"A. P. Gaikwad, R. R. Manza, and G. R. Manza. 2014. Automatic video scene segmentation to separate script and recognition. In 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). 225\u2013235."},{"key":"e_1_3_2_85_2","first-page":"433","article-title":"Review of scene text detection and recognition","author":"Lin H.","year":"2019","unstructured":"H. Lin, P. Yang, and F. Zhang. 2019. Review of scene text detection and recognition. Arch. Comput. Meth. Eng. 27 (2019), 433\u2013454. DOI:https:\/\/doi.org\/10.1007\/s11831-019-09315-1","journal-title":"Arch. Comput. Meth. Eng."},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2015.11.005"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.10.016"},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.166"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.07.008"},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2555080"},{"key":"e_1_3_2_91_2","doi-asserted-by":"crossref","unstructured":"Xinyu Zhou Cong Yao He Wen Yuzhi Wang Shuchang Zhou Weiran He and Jiajun Liang. 2017. EAST: An efficient and accurate scene text detector. arXiv:1704.03155v2 2017.","DOI":"10.1109\/CVPR.2017.283"},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_22"},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.452"},{"key":"e_1_3_2_94_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.388"},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00619"},{"key":"e_1_3_2_96_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.543"},{"key":"e_1_3_2_97_2","doi-asserted-by":"publisher","DOI":"10.5555\/3504035.3504864"},{"key":"e_1_3_2_98_2","doi-asserted-by":"publisher","DOI":"10.1117\/12.2325523"},{"key":"e_1_3_2_99_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2018.2802644"},{"issue":"2","key":"e_1_3_2_100_2","first-page":"47","article-title":"Scene text detection based on enhanced multi-channels MSER and a fast text grouping process","volume":"9","author":"Dai Y.","year":"2018","unstructured":"Y. Dai, Z. Wang, X. Zhao, and S. Shao. 2018. Scene text detection based on enhanced multi-channels MSER and a fast text grouping process. Int. J. Comput. Ling. Res. 9 2, (2018) 47\u201359.","journal-title":"Int. J. Comput. Ling. Res."},{"key":"e_1_3_2_101_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.09.089"},{"key":"e_1_3_2_102_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.02.061"},{"key":"e_1_3_2_103_2","article-title":"Multi-lingual text localization from camera captured images based on foreground homogeneity analysis","author":"Dutta N.","year":"2019","unstructured":"N. Dutta, N. Chakraborty, A. F. Mollah, S. Basu, and R. Sarkar. 2019. Multi-lingual text localization from camera captured images based on foreground homogeneity analysis. In Advances in Intelligent Systems and Computing, vol. 740. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-13-1280-9_15","journal-title":"Advances in Intelligent Systems and Computing"},{"key":"e_1_3_2_104_2","doi-asserted-by":"publisher","DOI":"10.4018\/IJCVIP.2019040104"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-1280-9_16"},{"key":"e_1_3_2_106_2","first-page":"1","volume-title":"Multimedia Tools and Applications","author":"Chakraborty N.","year":"2020","unstructured":"N. Chakraborty, A. Chatterjee, P. K. Singh, A. F. Mollah, and R. Sarkar. 2020. Application of daisy descriptor for language identification in the wild. In Multimedia Tools and Applications. Springer Publishers, 1\u201322. DOI:https:\/\/doi.org\/10.1007\/s11042-020-09728-2"},{"key":"e_1_3_2_107_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2017.09.005"},{"key":"e_1_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.5555\/1304595.1304703"},{"issue":"5","key":"e_1_3_2_109_2","first-page":"903","article-title":"Writer identification from offline isolated handwritten Gurumukhi characters","volume":"10","author":"Kalra K.","year":"2017","unstructured":"K. Kalra and S. Rani. 2017. Writer identification from offline isolated handwritten Gurumukhi characters. In Adv. Comput. Sci. Technol. 10, 5 (2017), 903\u2013914.","journal-title":"Adv. Comput. Sci. Technol."},{"issue":"13","key":"e_1_3_2_110_2","first-page":"1","article-title":"Writer identification using texture features in Kannada handwritten documents","volume":"6","author":"Bangarimath P.","year":"2018","unstructured":"P. Bangarimath, D. Bendigeri, and J. Pujari. 2018. Writer identification using texture features in Kannada handwritten documents. Int. J. Eng. Res. Technol. 6, 13 (2018), 1\u20135.","journal-title":"Int. J. Eng. Res. Technol."},{"key":"e_1_3_2_111_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACPR.2017.158"},{"key":"e_1_3_2_112_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2010.494"},{"key":"e_1_3_2_113_2","first-page":"65","volume-title":"ICDAR-2017 Workshop on Machine Learning","author":"Andrew C.","year":"2017","unstructured":"C. Andrew, S. Reddy, V. Pulabaigari, and U. Pal. 2017. Text independent writer identification for Telugu script using directional filter based features. In ICDAR-2017 Workshop on Machine Learning. 65\u201370."},{"key":"e_1_3_2_114_2","doi-asserted-by":"publisher","DOI":"10.1109\/DAS.2012.86"},{"key":"e_1_3_2_115_2","doi-asserted-by":"publisher","DOI":"10.5555\/938980.939531"},{"key":"e_1_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2005.231"},{"key":"e_1_3_2_117_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2011.295"},{"key":"e_1_3_2_118_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2011.296"},{"key":"e_1_3_2_119_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2013.221"},{"key":"e_1_3_2_120_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2015.7333942"},{"key":"e_1_3_2_121_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2011.286"},{"key":"e_1_3_2_122_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2017.236"},{"key":"e_1_3_2_123_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2017.237"},{"key":"e_1_3_2_124_2","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/978-981-10-3391-9_10","volume-title":"Advanced Computing and Systems for Security","author":"Sen S.","year":"2017","unstructured":"S. Sen, R. Sarkar, and K. Roy. 2017. An approach to stroke-based online handwritten Bangla character recognition. In Advanced Computing and Systems for Security. R. Chaki, K. Saeed, A. Cortesi, and N. Chaki (Eds.). Springer, 153\u2013163."},{"key":"e_1_3_2_125_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2018.02.008"},{"key":"e_1_3_2_126_2","doi-asserted-by":"publisher","DOI":"10.1109\/SCOPES.2016.7955866"},{"key":"e_1_3_2_127_2","doi-asserted-by":"publisher","DOI":"10.5555\/1778066.1778093"},{"key":"e_1_3_2_128_2","first-page":"1156","volume-title":"8th International Conference on Document Analysis and Recognition (ICDAR)","author":"Joshi N.","year":"2015","unstructured":"N. Joshi, G. Sita, A. G. Ramakrishnan, V. Deepu, and S. Madhvanath. 2015. Machine recognition of online handwritten Devanagari characters. In 8th International Conference on Document Analysis and Recognition (ICDAR). IEEE Computer Society, 1156\u20131160. DOI:http:\/\/dx.doi.org\/10.1109\/ICDAR.2005.156"},{"key":"e_1_3_2_129_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPCSim.2012.6266913"},{"key":"e_1_3_2_130_2","doi-asserted-by":"crossref","unstructured":"L. Lajish and S. K. Kopparapu. 2015. Online handwritten Devanagari stroke recognition using extended directional features. arXiv:1501.02887v1 [cs.CV] 11.","DOI":"10.1109\/ICSPCS.2014.7021063"},{"key":"e_1_3_2_131_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72847-4_48"},{"key":"e_1_3_2_132_2","doi-asserted-by":"publisher","DOI":"10.1109\/IWFHR.2004.80"},{"key":"e_1_3_2_133_2","doi-asserted-by":"publisher","DOI":"10.1109\/CISP.2008.297"},{"key":"e_1_3_2_134_2","doi-asserted-by":"publisher","DOI":"10.1145\/3282441"},{"key":"e_1_3_2_135_2","doi-asserted-by":"publisher","DOI":"10.1145\/2896318"},{"key":"e_1_3_2_136_2","doi-asserted-by":"publisher","DOI":"10.1145\/2345396.2345568"},{"key":"e_1_3_2_137_2","doi-asserted-by":"publisher","DOI":"10.1109\/RTEICT.2017.8256657"},{"key":"e_1_3_2_138_2","doi-asserted-by":"publisher","DOI":"10.1080\/03772063.2000.11416182"},{"key":"e_1_3_2_139_2","doi-asserted-by":"publisher","DOI":"10.1109\/INDCON.2012.6420691"},{"key":"e_1_3_2_140_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSIPR.2013.6497991"},{"key":"e_1_3_2_141_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-19403-0_32"},{"key":"e_1_3_2_142_2","doi-asserted-by":"publisher","DOI":"10.1109\/FIT.2014.61"},{"key":"e_1_3_2_143_2","first-page":"98","volume-title":"IAPR Conference on Machine Vision Applications (IAPR MVA)","author":"Husain A.","year":"2007","unstructured":"A. Husain, A. Sajjad, and F. Anwar. 2007. Online Urdu character recognition system. In IAPR Conference on Machine Vision Applications (IAPR MVA). 98\u2013101."},{"key":"e_1_3_2_144_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICET.2005.1558849"},{"key":"e_1_3_2_145_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCNT.2017.8203926"},{"key":"e_1_3_2_146_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIIP.2015.7414753"},{"key":"e_1_3_2_147_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICACCT.2018.8529410"},{"key":"e_1_3_2_148_2","doi-asserted-by":"publisher","DOI":"10.1515\/jisys-2017-0431"},{"issue":"39","key":"e_1_3_2_149_2","first-page":"1","article-title":"A study of different classifier combination approaches for handwritten Indic script recognition","volume":"4","author":"Mukhopadhyay A.","year":"2018","unstructured":"A. Mukhopadhyay, P. K. Singh, R. Sarkar, and M. Nasipuri. 2018. A study of different classifier combination approaches for handwritten Indic script recognition. J. Imag. 4, 39 (2018), 1\u201321. DOI:10.3390\/jimaging4020039","journal-title":"J. Imag."}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3476102","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3476102","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:02Z","timestamp":1750191122000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3476102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,3]]},"references-count":148,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,3,31]]}},"alternative-id":["10.1145\/3476102"],"URL":"https:\/\/doi.org\/10.1145\/3476102","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"type":"print","value":"2375-4699"},{"type":"electronic","value":"2375-4702"}],"subject":[],"published":{"date-parts":[[2021,11,3]]},"assertion":[{"value":"2020-01-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}