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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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 27; no. 5; pp. 777 - 792
Main Authors: Yefeng Zheng, Huiping Li, Doermann, D.
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.05.2005
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Description
Summary: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.
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ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2005.89