A parallel-line detection algorithm based on HMM decoding

The detection of groups of parallel lines is important in applications such as form processing and text (handwriting) extraction from 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...

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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 27; číslo 5; s. 777 - 792
Hlavní autoři:	Yefeng Zheng, Huiping Li, Doermann, D.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Los Alamitos, CA IEEE 01.05.2005 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Applied sciences Artificial Intelligence Automatic Data Processing - methods Computer Graphics Computer science; control theory; systems Context modeling Decoding Degradation Detection algorithms document image analysis Documentation - methods Exact sciences and technology Extraction Filtering form identification form processing form registration Handwriting hidden Markov model Hidden Markov models Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image quality Index Terms- Line detection Information Storage and Retrieval - methods Intelligence Markov Chains Mathematical models Models, Statistical Numerical Analysis, Computer-Assisted Optical character recognition software Pattern analysis Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Preprocessing Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted Subtraction Technique Text analysis Texts Viterbi algorithm Stochastic model Document image processing Line detection form registration form identification document image analysis Hidden Markov model Decoding Character recognition form processing Shape detection
ISSN:	0162-8828, 1939-3539
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Popis
Shrnutí:	The detection of groups of parallel lines is important in applications such as form processing and text (handwriting) extraction from 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 (such as skew correction and text filtering), we use trained hidden Markov models (HMM) to locate the optimal positions of all lines simultaneously on the horizontal or vertical projection profiles, based on the Viterbi decoding. The algorithm is trainable so it can be easily adapted to different application scenarios. The experiments conducted on known form processing and rule line detection show our method is robust, and achieves better results than other widely used line detection methods.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3
ISSN:	0162-8828 1939-3539
DOI:	10.1109/TPAMI.2005.89