{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T11:25:42Z","timestamp":1771327542743,"version":"3.50.1"},"reference-count":23,"publisher":"World Scientific Pub Co Pte Lt","issue":"05","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2012,8]]},"abstract":"<jats:p> Word spotting is the process of retrieving all instances of a queried keyword from a digital library of document images. In this paper we evaluate the performance of different word descriptors to assess the advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents. We compare four word representation models, namely sequence alignment using DTW as a baseline reference, a bag of visual words approach as statistical model, a pseudo-structural model based on a Loci features representation, and a structural approach where words are represented by graphs. The four approaches have been tested with two collections of historical data: the George Washington database and the marriage records from the Barcelona Cathedral. We experimentally demonstrate that statistical representations generally give a better performance, however it cannot be neglected that large descriptors are difficult to be implemented in a retrieval scenario where word spotting requires the indexation of data with million word images. <\/jats:p>","DOI":"10.1142\/s0218001412630025","type":"journal-article","created":{"date-parts":[[2012,8,26]],"date-time":"2012-08-26T22:33:43Z","timestamp":1346020423000},"page":"1263002","source":"Crossref","is-referenced-by-count":38,"title":["ON THE INFLUENCE OF WORD REPRESENTATIONS FOR HANDWRITTEN WORD SPOTTING IN HISTORICAL DOCUMENTS"],"prefix":"10.1142","volume":"26","author":[{"given":"JOSEP","family":"LLAD\u00d3S","sequence":"first","affiliation":[{"name":"Computer Vision Center, Computer Science Department, Edifici O, Universitat Aut\u00f2noma de Barcelona, 08193, Bellaterra, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"MAR\u00c7AL","family":"RUSI\u00d1OL","sequence":"additional","affiliation":[{"name":"Computer Vision Center, Computer Science Department, Edifici O, Universitat Aut\u00f2noma de Barcelona, 08193, Bellaterra, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ALICIA","family":"FORN\u00c9S","sequence":"additional","affiliation":[{"name":"Computer Vision Center, Computer Science Department, Edifici O, Universitat Aut\u00f2noma de Barcelona, 08193, Bellaterra, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"DAVID","family":"FERN\u00c1NDEZ","sequence":"additional","affiliation":[{"name":"Computer Vision Center, Computer Science Department, Edifici O, Universitat Aut\u00f2noma de Barcelona, 08193, Bellaterra, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ANJAN","family":"DUTTA","sequence":"additional","affiliation":[{"name":"Computer Vision Center, Computer Science Department, Edifici O, Universitat Aut\u00f2noma de Barcelona, 08193, Bellaterra, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2012,11,26]]},"reference":[{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.144"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21257-4_78"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.113"},{"key":"rf9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-88682-2_15"},{"key":"rf12","doi-asserted-by":"publisher","DOI":"10.1016\/0031-3203(94)00090-9"},{"key":"rf13","doi-asserted-by":"publisher","DOI":"10.1145\/582415.582418"},{"key":"rf14","first-page":"117","volume":"1","author":"J\u00e9gou H.","journal-title":"IEEE Trans. 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