Paper Info
Title
Detecting and locating partially specified keywords in scanned images using hidden Markov models
Abstract
A hidden Markov model (HMM) based system for detecting locating, or spotting, user-specified keywords in scanned images is described. The system is font-independent, and no pre-segmentation of text and graphics is required. The bounding boxes of potential lines of text are extracted from the image using morphology. Feature vectors based on the external shape and internal structure of characters are computed for each bounding box. A keyword HMM is created by concatenating appropriate context-dependent character HMMs. The non-keyword HMM is based on context-dependent sub-character models. Keywords are spotted using Viterbi decoding on an HMM network created from the keyword and non-keyword HMMs. This model allows detection of keywords embedded in a line without pre-segmentation of the line into words or characters. Thus keywords may be specified by a baseform and variants of the keyword can be detected
Year
DOI
Venue
1993
10.1109/ICDAR.1993.395765
Tsukuba Science City
Keywords
Field
DocType
Viterbi decoding,feature extraction,hidden Markov models,word processing,Viterbi decoding,bounding box,bounding boxes,context-dependent character HMMs,context-dependent sub-character models,external shape,feature vectors,font-independent,hidden Markov model,internal structure,keyword HMM,morphology,partially specified keywords,scanned images,user-specified keywords
Graphics,Computer vision,Feature vector,Pattern recognition,Computer science,Image retrieval,Feature extraction,Image segmentation,Artificial intelligence,Hidden Markov model,Word processing,Minimum bounding box
Conference
ISBN
Citations 
PageRank 
0-8186-4960-7
26
2.72
References 
Authors
9
3
Name
Order
Citations
PageRank
Francine Chen11218153.96
Lynn Wilcox21330180.16
Bloomberg, Dan S.3394.43