{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:16:13Z","timestamp":1750306573101,"version":"3.41.0"},"reference-count":43,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2014,10,3]],"date-time":"2014-10-03T00:00:00Z","timestamp":1412294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100007302","name":"HP Labs","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007302","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Transactions on Asian Language Information Processing"],"published-print":{"date-parts":[[2014,10,3]]},"abstract":"<jats:p>\n            Writer-specific character writing variations such as those of stroke order and stroke number are an important source of variability in the input when handwriting is captured \u201conline\u201d via a stylus and a challenge for robust online recognition of handwritten characters and words. It has been shown by several studies that explicit modeling of character allographs is important for achieving high recognition accuracies in a writer-independent recognition system. While previous approaches have relied on unsupervised clustering at the character or stroke level to find the allographs of a character, in this article we propose the use of\n            <jats:italic>constrained clustering<\/jats:italic>\n            using automatically derived domain constraints to find a minimal set of stroke clusters. The allographs identified have been applied to Devanagari character recognition using Hidden Markov Models and Nearest Neighbor classifiers, and the results indicate substantial improvement in recognition accuracy and\/or reduction in memory and computation time when compared to alternate modeling techniques.\n          <\/jats:p>","DOI":"10.1145\/2629622","type":"journal-article","created":{"date-parts":[[2014,10,7]],"date-time":"2014-10-07T12:57:47Z","timestamp":1412686667000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Allograph modeling for online handwritten characters in devanagari using constrained stroke clustering"],"prefix":"10.1145","volume":"13","author":[{"given":"Bharath","family":"A.","sequence":"first","affiliation":[{"name":"Hewlett-Packard Labs, Bangalore, India"}]},{"given":"Sriganesh","family":"Madhvanath","sequence":"additional","affiliation":[{"name":"Hewlett-Packard Labs, Bangalore, India"}]}],"member":"320","published-online":{"date-parts":[[2014,10,3]]},"reference":[{"volume-title":"Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR\u201907)","author":"Babu V. 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Ph.D. Dissertation Michigan State Univ.   S. D. Connell. 2000. Online handwriting recognition using multiple pattern class models. Ph.D. Dissertation Michigan State Univ."},{"volume-title":"Proceedings of the 14th International Conference on Pattern Recognition (ICPR\u201998)","author":"Connell S. D.","key":"e_1_2_1_13_1"},{"volume-title":"Proceedings of the 15th International Conference on Pattern Recognition (ICPR\u201900)","author":"Connell S. D.","key":"e_1_2_1_14_1"},{"key":"e_1_2_1_15_1","unstructured":"F. Coulmas. 1996. The Blackwell Encyclopedia of Writing Systems. Blackwell Oxford.  F. Coulmas. 1996. The Blackwell Encyclopedia of Writing Systems . Blackwell Oxford."},{"key":"e_1_2_1_16_1","unstructured":"Richard O. Duda Peter E. Hart and David G. Stork. 2001. Pattern Classification. Wiley.   Richard O. Duda Peter E. Hart and David G. Stork. 2001. Pattern Classification . 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Swethalakshmi. 2007. Online handwritten character recognition for Devanagari and Tamil scripts using support vector machines. Master\u2019s thesis Indian Institute of Technology Madras India.  H. Swethalakshmi. 2007. Online handwritten character recognition for Devanagari and Tamil scripts using support vector machines. Master\u2019s thesis Indian Institute of Technology Madras India."},{"volume-title":"Proceedings of the 4th International Conference on Document Analysis and Recognition (ICDAR\u201997)","author":"Takahashi K.","key":"e_1_2_1_34_1"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.5555\/839279.840372"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.5555\/851040.856878"},{"volume-title":"Proceedings of the 16th International Conference on Pattern Recognition (ICPR\u201902)","author":"Vuori V.","key":"e_1_2_1_37_1"},{"volume-title":"Proceedings of the 4th International Conference on Document Analysis and Recognition (ICDAR\u201997)","author":"Vuurpijl L.","key":"e_1_2_1_38_1"},{"volume-title":"Progress in Handwriting Recognition","author":"Vuurpijl L. 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