{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:32:44Z","timestamp":1777703564357,"version":"3.51.4"},"reference-count":15,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2015,5,18]],"date-time":"2015-05-18T00:00:00Z","timestamp":1431907200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,5,24]]},"abstract":"<jats:p>\n                    Counting the number of words and lines that a user reads is important for many educational purposes \u2013 e.g., the reading speed is a key factor to improve learning, intelligent systems can suggest text that must be read to achieve a determined learning objective. The eye tracking technology is commonly used to analyze the user reading habits. Counting the number of read words could be hard when the readings are obtained from imprecise eye tracking data \u2013 e.g., eye tracking calibration difficulties. Approaches that find patterns from saccades and fixations usually fail to solve the problem in such conditions. This paper introduces the\n                    <jats:sc>Cowl<\/jats:sc>\n                    approach, which deals with the imprecision problem by associating the eye tracking data with points obtained from character recognition. To detect text lines truly read, the problem is stated as one of merging two hypothetical lines and it is solved by a Bayesian approach. Tests show that the proposed approach shows high performance, reaching average precision rates up to 0.866 for recall 0.976 \u2013 in the case of text with different orientations.\n                  <\/jats:p>","DOI":"10.3233\/jifs-169498","type":"journal-article","created":{"date-parts":[[2018,5,22]],"date-time":"2018-05-22T10:54:00Z","timestamp":1526986440000},"page":"3147-3154","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Counting the number of words and lines read by fusing eye tracking and character recognition data: A Bayes factor approach"],"prefix":"10.1177","volume":"34","author":[{"given":"Carlos","family":"Lara-\u00c1lvarez","sequence":"first","affiliation":[{"name":"CONACYT Research Fellow \u2013 CIMAT Zacatecas, Av. Universidad 222, Frac. La Loma, Zacatecas, M\u00e9xico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tania","family":"Reyes","sequence":"additional","affiliation":[{"name":"HCI Lab \u2013 CIMAT Zacatecas, Av. Universidad 222, Frac. La Loma, Zacatecas, M\u00e9xico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hector","family":"Rodr\u00edguez-Rangel","sequence":"additional","affiliation":[{"name":"Instituto Tecnol\u00f3gico de Culiac\u00e1n, Divisi\u00f3n de Estudios de Posgrado e Investigaci\u00f3n, Juan de Dios B\u00e1tiz 310 pte. Col. 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