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| 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 the Bayesian Active Learning by Disagreement (BALD) model for keyword spotting in handwritten documents. In the context of keyword spotting in handwritten documents, the background text is all regions in the document that do not contain the keywords. The model tries to learn certain characteristics of the keyword and background text in an active learning framework. It takes into account the local character level scores and global word level scores to distinguish keywords from non-keywords. We propose to apply the bayesian active learning strategy to identify the regions of sample space from which more meaningful labeled samples of keywords and non-keywords can be extracted. This work is an extension to our previous work which used a variational dynamic background model to model the large variations of background text. The approach has been tested on IAM dataset for English. The results show that a decent background model can be learned in a more quicker and efficient manner using the BALD framework. The approach outperforms our prior work and other state of the art approaches. |
| Starting Page | 2041 |
| Ending Page | 2046 |
| File Size | 520298 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479952090 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2014.356 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-24 |
| Publisher Place | Sweden |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Bayes methods Hidden Markov models Logistics Mathematical model Feature extraction Image segmentation Entropy Bayesian Active Learning Spotting Handwriting Recognition |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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