NDLI: A Bayesian Approach to Script Independent Multilingual Keyword Spotting

Content Provider	IEEE Xplore Digital Library
Author	Kumar, G. Govindaraju, V.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Univ. at Buffalo, Amherst, NY, USA (Kumar, G.; Govindaraju, V.)
Abstract	We propose a script independent Bayesian framework for keyword spotting in multilingual handwritten documents. The approach relies on local character level score and global word level hypothesis scores and learns a Bayesian logistic regression classifier to distinguish between keywords and non-keywords. In a Bayesian formulation of logistic regression, the integral over weights becomes intractable. Variational approximation is used for inference. In order to learn a robust classifier with minimal number of samples, we apply Bayesian active learning framework to request labels for those word images which provide maximum information gain in improving the classifier. We evaluate our system on multilingual datasets, publicly available IAM dataset for English, AMA for Arabic and LAW dataset for Devanagiri. The system is also evaluated on a synthetic multilingual dataset prepared by combining samples from IAM, AMA and LAW datasets. The results are comparable with the state of art multilingual keyword spotting framework.
Starting Page	357
Ending Page	362
File Size	465542
Page Count	6
File Format	PDF
ISBN	9781479943357
ISSN	21676445
e-ISBN	9781479943340
DOI	10.1109/ICFHR.2014.66
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-01
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Bayes methods Feature extraction Logistics Image segmentation Image recognition Approximation methods Bayesian Active Learning Spotting Handwritten Multilingual Documents Script Independent
Content Type	Text
Resource Type	Article

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