Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDAR.2011.250
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dc.titleVideo script identification based on text lines
dc.contributor.authorPhan, T.Q.
dc.contributor.authorShivakumara, P.
dc.contributor.authorDing, Z.
dc.contributor.authorLu, S.
dc.contributor.authorTan, C.L.
dc.date.accessioned2013-07-04T08:37:56Z
dc.date.available2013-07-04T08:37:56Z
dc.date.issued2011
dc.identifier.citationPhan, T.Q., Shivakumara, P., Ding, Z., Lu, S., Tan, C.L. (2011). Video script identification based on text lines. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR : 1240-1244. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDAR.2011.250
dc.identifier.isbn9780769545202
dc.identifier.issn15205363
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41876
dc.description.abstractIn this paper, we present a new method for video script identification which is essential before choosing an appropriate OCR engine for identifying text lines when a video frame contains more than one language. The input for script identification is the text lines obtained by our text detection method. We extract upper and lower extreme points for each connected component of Canny edges of text lines. The extracted points are connected to study the behavior of upper and lower lines. The direction of each 10-pixel segment of the lines is determined using PCA. The average angle of the segments of the upper and lower lines is computed to study the smoothness and cursiveness of the lines. In addition, to discriminate the scripts accurately, the method divides a text line into five equal zones horizontally to study the smoothness and cursiveness of the upper and lower lines of each zone. We evaluate the method by conducting experiments on different combinations of languages such as English and Chinese, English and Tamil, Chinese and Tamil, and English, Chinese and Tamil. © 2011 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICDAR.2011.250
dc.sourceScopus
dc.subjectCursiveness
dc.subjectSmoothness
dc.subjectUpper and lower points
dc.subjectVideo scrpt line identification
dc.subjectVideo text line
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1109/ICDAR.2011.250
dc.description.sourcetitleProceedings of the International Conference on Document Analysis and Recognition, ICDAR
dc.description.page1240-1244
dc.identifier.isiut000343450700244
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