A PARALLEL LINE DETECTION ALGORITHM BASED ON HMM DECODING

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dc.contributor.authorZheng, Yefengen_US
dc.contributor.authorLi, Huipingen_US
dc.contributor.authorDoermann, Daviden_US
dc.date.accessioned2004-05-31T23:34:20Z
dc.date.available2004-05-31T23:34:20Z
dc.date.created2003-12en_US
dc.date.issued2003-12-18en_US
dc.description.abstractThe detection of groups of parallel lines is important in applications such as form processing and text (handwriting) extraction in rule lined paper. These tasks can be very challenging in degraded documents where the lines are severely broken. In this paper we propose a novel model-based method which incorporates high level context to detect these lines. After preprocessing and skew correction, we use trained Hidden Markov Models (HMM) to locate the optimal positions of all lines simultaneously, based on the Viterbi decoding. The algorithm is trainable, therefore, it can easily be adapted to different application scenarios. The experiments conducted on known form processing and rule line detection show our method is robust, and achieved better results than other widely used line detection methods, such as the Hough transform, projection or vectorization-based methods. LAMP-TR-109 UMIACS-TR-2003-113en_US
dc.format.extent738423 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/1327
dc.language.isoen_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-4545en_US
dc.relation.ispartofseriesLAMP-TR-109en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-2003-113en_US
dc.titleA PARALLEL LINE DETECTION ALGORITHM BASED ON HMM DECODINGen_US
dc.typeTechnical Reporten_US

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