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Word spotting in scanned images using hidden Markov models

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3 Author(s)
Chen, F.R. ; Xerox Palo Alto Res. Center, CA, USA ; Wilcox, L.D. ; Bloomberg, D.S.

A hidden-Markov-model (HMM)-based system for font-independent spotting of user-specified keywords in a scanned image is described. Word bounding boxes of potential keywords are extracted from the image using a morphology-based preprocessor. Feature vectors based on the external shape and internal structure of the word are computed over vertical columns of pixels in a word bounding box. For each user-specified keyword, an HMM is created by concatenating appropriate context-dependent character HMMs. Nonkeywords are modeled using an HMM based on context-dependent subcharacter models. Keyword spotting is performed using a Viterbi search through the HMM network created by connecting the keyword and nonkeyword HMMs in parallel. Applications of word-image spotting include information filtering in images from facsimile and copy machines, and information retrieval from text image databases.<>

Published in:

Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on  (Volume:5 )

Date of Conference:

27-30 April 1993

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